Prompt:
Explain this yaml code:

defaultConstraints:
  - maxSkew: 3
    topologyKey: "kubernetes.io/hostname"
    whenUnsatisfiable: ScheduleAnyway
  - maxSkew: 5
    topologyKey: "topology.kubernetes.io/zone"
    whenUnsatisfiable: ScheduleAnyway

Response: This yaml code 10.5 - Pod Topology Spread Constraints | Also, the legacy SelectorSpread plugin, which provides an equivalent behavior, is disabled by default.


Prompt:
Complete this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler

Response:
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List

Context: 10.5 - Pod Topology Spread Constraints | In Kubernetes, inter-Pod affinity and anti-affinity control how Pods are scheduled in relation to one another - either more packed or more scattered.


Prompt:
Explain this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List

Response: This yaml code 10.5 - Pod Topology Spread Constraints | In Kubernetes, inter-Pod affinity and anti-affinity control how Pods are scheduled in relation to one another - either more packed or more scattered.


Prompt:
Complete this yaml code:

tolerations:
- key: "key1"

Response:
  operator: "Equal"
  value: "value1"
  effect: "NoSchedule"

Context: 10.6 - Taints and Tolerations | 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 byp


Prompt:
Explain this yaml code:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoSchedule"

Response: This yaml code 10.6 - Taints and Tolerations | 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 byp


Prompt:
Complete this yaml code:

tolerations:
- key: "key1"

Response:
  operator: "Exists"
  effect: "NoSchedule"


Prompt:
Explain this yaml code:

tolerations:
- key: "key1"
  operator: "Exists"
  effect: "NoSchedule"

Response: This yaml code 10.6 - Taints and Tolerations | 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 byp


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:

Response:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  tolerations:
  - key: "example-key"
    operator: "Exists"
    effect: "NoSchedule"

Context: 10.6 - Taints and Tolerations | The default value for operator is Equal.


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  tolerations:
  - key: "example-key"
    operator: "Exists"
    effect: "NoSchedule"

Response: This yaml code 10.6 - Taints and Tolerations | The default value for operator is Equal.


Prompt:
Explain this shell code:

kubectl taint nodes node1 key1=value1:NoSchedule
kubectl taint nodes node1 key1=value1:NoExecute
kubectl taint nodes node1 key2=value2:NoSchedule

Response: This shell code 10.6 - Taints and Tolerations | And a pod has two tolerations:

Context: 10.6 - Taints and Tolerations | And a pod has two tolerations:


Prompt:
Complete this yaml code:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"

Response:
  effect: "NoSchedule"
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"

Context: 10.6 - Taints and Tolerations | 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


Prompt:
Explain this yaml code:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoSchedule"
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"

Response: This yaml code 10.6 - Taints and Tolerations | 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


Prompt:
Complete this yaml code:

tolerations:
- key: "key1"
  operator: "Equal"

Response:
  value: "value1"
  effect: "NoExecute"
  tolerationSeconds: 3600

Context: 10.6 - Taints and Tolerations | 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, t


Prompt:
Explain this yaml code:

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"
  tolerationSeconds: 3600

Response: This yaml code 10.6 - Taints and Tolerations | 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, t


Prompt:
Explain this bash code:

kubectl taint nodes nodename dedicated=groupName:NoSchedule

Response: This bash code 10.6 - Taints and Tolerations | 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

Context: 10.6 - Taints and Tolerations | 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


Prompt:
Explain this bash code:

kubectl taint nodes nodename special=true:NoSchedule

Response: This bash code 10.6 - Taints and Tolerations | Taint based Evictions: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section.

Context: 10.6 - Taints and Tolerations | Taint based Evictions: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section.


Prompt:
Explain this bash code:

kubectl taint nodes nodename special=true:PreferNoSchedule

Response: This bash code 10.6 - Taints and Tolerations | Taint based Evictions: A per-pod-configurable eviction behavior when there are node problems, which is described in the next section.


Prompt:
Complete this yaml code:

tolerations:
- key: "node.kubernetes.io/unreachable"

Response:
  operator: "Exists"
  effect: "NoExecute"
  tolerationSeconds: 6000

Context: 10.6 - Taints and Tolerations | 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


Prompt:
Explain this yaml code:

tolerations:
- key: "node.kubernetes.io/unreachable"
  operator: "Exists"
  effect: "NoExecute"
  tolerationSeconds: 6000

Response: This yaml code 10.6 - Taints and Tolerations | 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


Prompt:
Explain this go code:

func ScoreNode(_ *v1.pod, n *v1.Node) (int, error) {
    return getBlinkingLightCount(n)
}

Response: This go code 10.7 - Scheduling Framework | However, the maximum count of blinking lights may be small compared to NodeScoreMax. To fix this, BlinkingLightScorer should also register for this extension point.

Context: 10.7 - Scheduling Framework | However, the maximum count of blinking lights may be small compared to NodeScoreMax. To fix this, BlinkingLightScorer should also register for this extension point.


Prompt:
Complete this go code:

func NormalizeScores(scores map[string]int) {
    highest := 0
    for _, score := range scores {
        highest = max(highest, score)

Response:
    }
    for node, score := range scores {
        scores[node] = score*NodeScoreMax/highest
    }
}

Context: 10.7 - Scheduling Framework | If any NormalizeScore plugin returns an error, the scheduling cycle is aborted.


Prompt:
Explain this go code:

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
    }
}

Response: This go code 10.7 - Scheduling Framework | If any NormalizeScore plugin returns an error, the scheduling cycle is aborted.


Prompt:
Complete this go code:

type Plugin interface {
    Name() string
}

type QueueSortPlugin interface {
    Plugin
    Less(*v1.pod, *v1.pod) bool

Response:
}

type PreFilterPlugin interface {
    Plugin
    PreFilter(context.Context, *framework.CycleState, *v1.pod) error
}

// ...

Context: 10.7 - Scheduling Framework | You can enable or disable plugins in the scheduler configuration. If you are using Kubernetes v1.18 or later, most scheduling plugins are in use and enabled by default.


Prompt:
Explain this go code:

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
}

// ...

Response: This go code 10.7 - Scheduling Framework | You can enable or disable plugins in the scheduler configuration. If you are using Kubernetes v1.18 or later, most scheduling plugins are in use and enabled by default.


Prompt:
Complete this yaml code:

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

Response:
          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

Context: 10.8 - Dynamic Resource Allocation | 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


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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


Prompt:
Complete this yaml code:

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

Response:
  # 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:
        boolean: true

Context: 10.8 - Dynamic Resource Allocation | 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


Prompt:
Explain this yaml code:

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:
        boolean: true

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: pod-with-cats

Response:
spec:
  nodeSelector:
    kubernetes.io/hostname: name-of-the-intended-node
  ...

Context: 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: pod-with-cats
spec:
  nodeSelector:
    kubernetes.io/hostname: name-of-the-intended-node
  ...

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: large-black-cat-claim-template
spec:
  spec:

Response:
    devices:
      requests:
      - name: req-0
        exactly:
          deviceClassName: resource.example.com
          allocationMode: All
          adminAccess: true

Context: 10.8 - Dynamic Resource Allocation | If this feature is disabled, the adminAccess field will be removed automatically when creating such a ResourceClaim.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.8 - Dynamic Resource Allocation | If this feature is disabled, the adminAccess field will be removed automatically when creating such a ResourceClaim.


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1
kind: DeviceClass
metadata:
  name: gpu.example.com
spec:

Response:
  selectors:
  - cel:
      expression: device.driver == 'gpu.example.com' && device.attributes['gpu.example.com'].type
        == 'gpu'
  extendedResourceName: example.com/gpu

Context: 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Complete this yaml code:

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
  sharedCounters:
  - name: gpu-1-counters
    counters:

Response:
      memory:
        value: 8Gi
  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

Context: 10.8 - Dynamic Resource Allocation | Partitionable devices is an alpha feature and only enabled when the DRAPartitionableDevices feature gate is enabled in the kube-apiserver and kube-scheduler.


Prompt:
Explain this yaml code:

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
  sharedCounters:
  - name: gpu-1-counters
    counters:
      memory:
        value: 8Gi
  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

Response: This yaml code 10.8 - Dynamic Resource Allocation | Partitionable devices is an alpha feature and only enabled when the DRAPartitionableDevices feature gate is enabled in the kube-apiserver and kube-scheduler.


Prompt:
Complete this yaml code:

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:

Response:
  - name: eth1
    allowMultipleAllocations: true
    attributes:
      name:
        string: "eth1"
    capacity:
      bandwidth:
        requestPolicy:
          default: "1M"
          validRange:
            min: "1M"
            step: "8"
        value: "10G"

Context: 10.8 - Dynamic Resource Allocation | The consumable capacity can be requested as shown in the below example.


Prompt:
Explain this yaml code:

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"

Response: This yaml code 10.8 - Dynamic Resource Allocation | The consumable capacity can be requested as shown in the below example.


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: bandwidth-claim-template
spec:
  spec:
    devices:

Response:
      requests:
      - name: req-0
        exactly:
        - deviceClassName: resource.example.com
          capacity:
            requests:
              bandwidth: 1G

Context: 10.8 - Dynamic Resource Allocation | The allocation result will include the consumed capacity and the identifier of the share.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.8 - Dynamic Resource Allocation | The allocation result will include the consumed capacity and the identifier of the share.


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceClaim
...
status:
  allocation:

Response:
    devices:
      results:
      - consumedCapacity:
          bandwidth: 1G
        device: eth1
        shareID: "a671734a-e8e5-11e4-8fde-42010af09327"

Context: 10.8 - Dynamic Resource Allocation | 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-allocatab


Prompt:
Explain this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceClaim
...
status:
  allocation:
    devices:
      results:
      - consumedCapacity:
          bandwidth: 1G
        device: eth1
        shareID: "a671734a-e8e5-11e4-8fde-42010af09327"

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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-allocatab


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1alpha3
kind: DeviceTaintRule
metadata:
  name: example
spec:
  # The entire hardware installation for this
  # particular driver is broken.

Response:
  # Evict all pods and don't schedule new ones.
  deviceSelector:
    driver: dra.example.com
  taint:
    key: dra.example.com/unhealthy
    value: Broken
    effect: NoExecute

Context: 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.8 - Dynamic Resource Allocation | 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.


Prompt:
Complete this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: gpu-slice
spec:
  driver: dra.example.com
  nodeSelector:
    accelerator-type: high-performance
  pool:
    name: gpu-pool
    generation: 1
    resourceSliceCount: 1

Response:
  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

Context: 10.8 - Dynamic Resource Allocation | This example ResourceSlice has the following properties:


Prompt:
Explain this yaml code:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: gpu-slice
spec:
  driver: dra.example.com
  nodeSelector:
    accelerator-type: 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

Response: This yaml code 10.8 - Dynamic Resource Allocation | This example ResourceSlice has the following properties:


Prompt:
Explain this bash code:

kubectl get pods -n kube-system | grep kube-scheduler

Response: This bash code 10.9 - Scheduler Performance Tuning | to verify that the kube-scheduler component is healthy.

Context: 10.9 - Scheduler Performance Tuning | to verify that the kube-scheduler component is healthy.


Prompt:
Complete this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1alpha1
kind: KubeSchedulerConfiguration
algorithmSource:
  provider: DefaultProvider

Response:
...

percentageOfNodesToScore: 50

Context: 10.9 - Scheduler Performance Tuning | 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.


Prompt:
Explain this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1alpha1
kind: KubeSchedulerConfiguration
algorithmSource:
  provider: DefaultProvider

...

percentageOfNodesToScore: 50

Response: This yaml code 10.9 - Scheduler Performance Tuning | 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.


Prompt:
Explain this text code:

Zone 1: Node 1, Node 2, Node 3, Node 4
Zone 2: Node 5, Node 6

Response: This text code 10.9 - Scheduler Performance Tuning | The Scheduler evaluates feasibility of the nodes in this order:

Context: 10.9 - Scheduler Performance Tuning | The Scheduler evaluates feasibility of the nodes in this order:


Prompt:
Complete this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
  - args:
      scoringStrategy:
        resources:
        - name: cpu

Response:
          weight: 1
        - name: memory
          weight: 1
        - name: intel.com/foo
          weight: 3
        - name: intel.com/bar
          weight: 3
        type: MostAllocated
    name: NodeResourcesFit

Context: 10.10 - Resource Bin Packing | To learn more about other parameters and their default configuration, see the API documentation for NodeResourcesFitArgs.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.10 - Resource Bin Packing | To learn more about other parameters and their default configuration, see the API documentation for NodeResourcesFitArgs.


Prompt:
Complete this yaml code:

apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
  - args:
      scoringStrategy:
        resources:
        - name: intel.com/foo
          weight: 3

Response:
        - name: intel.com/bar
          weight: 3
        requestedToCapacityRatio:
          shape:
          - utilization: 0
            score: 0
          - utilization: 100
            score: 10
        type: RequestedToCapacityRatio
    name: NodeResourcesFit

Context: 10.10 - Resource Bin Packing | Referencing the KubeSchedulerConfiguration file with the kube-scheduler flag --config=/path/to/config/file will pass the configuration to the scheduler.


Prompt:
Explain this yaml code:

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

Response: This yaml code 10.10 - Resource Bin Packing | Referencing the KubeSchedulerConfiguration file with the kube-scheduler flag --config=/path/to/config/file will pass the configuration to the scheduler.


Prompt:
Complete this yaml code:

shape:
  - utilization: 0

Response:
    score: 0
  - utilization: 100
    score: 10

Context: 10.10 - Resource Bin Packing | 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 follo


Prompt:
Explain this yaml code:

shape:
  - utilization: 0
    score: 0
  - utilization: 100
    score: 10

Response: This yaml code 10.10 - Resource Bin Packing | 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 follo


Prompt:
Complete this yaml code:

shape:
  - utilization: 0

Response:
    score: 10
  - utilization: 100
    score: 0

Context: 10.10 - Resource Bin Packing | resources is an optional parameter which defaults to:


Prompt:
Explain this yaml code:

shape:
  - utilization: 0
    score: 10
  - utilization: 100
    score: 0

Response: This yaml code 10.10 - Resource Bin Packing | resources is an optional parameter which defaults to:


Prompt:
Complete this yaml code:

resources:
  - name: cpu

Response:
    weight: 1
  - name: memory
    weight: 1

Context: 10.10 - Resource Bin Packing | It can be used to add extended resources as follows:


Prompt:
Explain this yaml code:

resources:
  - name: cpu
    weight: 1
  - name: memory
    weight: 1

Response: This yaml code 10.10 - Resource Bin Packing | It can be used to add extended resources as follows:


Prompt:
Complete this yaml code:

resources:
  - name: intel.com/foo
    weight: 5

Response:
  - name: cpu
    weight: 3
  - name: memory
    weight: 1

Context: 10.10 - Resource Bin Packing | The weight parameter is optional and is set to 1 if not specified. Also, the weight cannot be set to a negative value.


Prompt:
Explain this yaml code:

resources:
  - name: intel.com/foo
    weight: 5
  - name: cpu
    weight: 3
  - name: memory
    weight: 1

Response: This yaml code 10.10 - Resource Bin Packing | The weight parameter is optional and is set to 1 if not specified. Also, the weight cannot be set to a negative value.


Prompt:
Complete this text code:

Available:
  intel.com/foo: 4
  memory: 1 GB
  cpu: 8

Response:
Used:
  intel.com/foo: 1
  memory: 256MB
  cpu: 1

Context: 10.10 - Resource Bin Packing | Node score:


Prompt:
Explain this text code:

Available:
  intel.com/foo: 4
  memory: 1 GB
  cpu: 8

Used:
  intel.com/foo: 1
  memory: 256MB
  cpu: 1

Response: This text code 10.10 - Resource Bin Packing | Node score:


Prompt:
Complete this text code:

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

Response:
               = 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

Context: 10.10 - Resource Bin Packing | Node 2 spec:


Prompt:
Explain this text code:

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

Response: This text code 10.10 - Resource Bin Packing | Node 2 spec:


Prompt:
Complete this text code:

Available:
  intel.com/foo: 8
  memory: 1GB
  cpu: 8

Response:
Used:
  intel.com/foo: 2
  memory: 512MB
  cpu: 6


Prompt:
Explain this text code:

Available:
  intel.com/foo: 8
  memory: 1GB
  cpu: 8
Used:
  intel.com/foo: 2
  memory: 512MB
  cpu: 6

Response: This text code 10.10 - Resource Bin Packing | Node score:


Prompt:
Complete this text code:

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

Response:
               = 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

Context: 10.10 - Resource Bin Packing | 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 o


Prompt:
Explain this text code:

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

Response: This text code 10.10 - Resource Bin Packing | 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 o


Prompt:
Complete this yaml code:

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:

Response:
  name: high-priority
value: 1000000
globalDefault: false
description: "This priority class should be used for XYZ service pods only."

Context: 10.11 - Pod Priority and Preemption | 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


Prompt:
Explain this yaml code:

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."

Response: This yaml code 10.11 - Pod Priority and Preemption | 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


Prompt:
Complete this yaml code:

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority-nonpreempting

Response:
value: 1000000
preemptionPolicy: Never
globalDefault: false
description: "This priority class will not cause other pods to be preempted."

Context: 10.11 - Pod Priority and Preemption | 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 fiel


Prompt:
Explain this yaml code:

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."

Response: This yaml code 10.11 - Pod Priority and Preemption | 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 fiel


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test

Response:
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  priorityClassName: high-priority

Context: 10.11 - Pod Priority and Preemption | 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


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  priorityClassName: high-priority

Response: This yaml code 10.11 - Pod Priority and Preemption | 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


Prompt:
Explain this text code:

local volumes + logs and a writable layer of all containers

Response: This text code 10.12 - Node-pressure Eviction | If nodefs triggers evictions, the kubelet sorts pods based on nodefs usage (local volumes + logs of all containers).

Context: 10.12 - Node-pressure Eviction | If nodefs triggers evictions, the kubelet sorts pods based on nodefs usage (local volumes + logs of all containers).


Prompt:
Explain this text code:

local volumes + logs and a writable layer of all containers

Response: This text code 10.12 - Node-pressure Eviction | If imagefs triggers evictions, the kubelet sorts pods based on the storage of images rank, which represents the disk usage of a given image.

Context: 10.12 - Node-pressure Eviction | If imagefs triggers evictions, the kubelet sorts pods based on the storage of images rank, which represents the disk usage of a given image.


Prompt:
Complete this yaml code:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
evictionHard:
  memory.available: "500Mi"
  nodefs.available: "1Gi"

Response:
  imagefs.available: "100Gi"
evictionMinimumReclaim:
  memory.available: "0Mi"
  nodefs.available: "500Mi"
  imagefs.available: "2Gi"

Context: 10.12 - Node-pressure Eviction | 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 minimu


Prompt:
Explain this yaml code:

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"

Response: This yaml code 10.12 - Node-pressure Eviction | 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 minimu


Prompt:
Explain this none code:

--eviction-hard=memory.available<500Mi
--system-reserved=memory=1.5Gi

Response: This none code 10.12 - Node-pressure Eviction | 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.

Context: 10.12 - Node-pressure Eviction | 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.


Prompt:
Explain this text code:

10% of the total memory + the eviction threshold amount

Response: This text code 10.12 - Node-pressure Eviction | 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 tri

Context: 10.12 - Node-pressure Eviction | 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 tri


Prompt:
Complete this json code:

{
  "apiVersion": "policy/v1",
  "kind": "Eviction",
  "metadata": {

Response:
    "name": "quux",
    "namespace": "default"
  }
}

Context: 10.13 - API-initiated Eviction | Note:Deprecated in v1.22 in favor of policy/v1{ "apiVersion": "policy/v1beta1", "kind": "Eviction", "metadata": { "name": "quux", "namespace": "default" } }


Prompt:
Explain this json code:

{
  "apiVersion": "policy/v1",
  "kind": "Eviction",
  "metadata": {
    "name": "quux",
    "namespace": "default"
  }
}

Response: This json code 10.13 - API-initiated Eviction | Note:Deprecated in v1.22 in favor of policy/v1{ "apiVersion": "policy/v1beta1", "kind": "Eviction", "metadata": { "name": "quux", "namespace": "default" } }


Prompt:
Complete this json code:

{
  "apiVersion": "policy/v1beta1",
  "kind": "Eviction",
  "metadata": {

Response:
    "name": "quux",
    "namespace": "default"
  }
}

Context: 10.13 - API-initiated Eviction | Alternatively, you can attempt an eviction operation by accessing the API using curl or wget, similar to the following example:


Prompt:
Explain this json code:

{
  "apiVersion": "policy/v1beta1",
  "kind": "Eviction",
  "metadata": {
    "name": "quux",
    "namespace": "default"
  }
}

Response: This json code 10.13 - API-initiated Eviction | Alternatively, you can attempt an eviction operation by accessing the API using curl or wget, similar to the following example:


Prompt:
Explain this bash code:

curl -v -H 'Content-type: application/json' https://your-cluster-api-endpoint.example/api/v1/namespaces/default/pods/quux/eviction -d @eviction.json

Response: This bash code 10.13 - API-initiated Eviction | When you request an eviction using the API, the API server performs admission checks and responds in one of the following ways:

Context: 10.13 - API-initiated Eviction | When you request an eviction using the API, the API server performs admission checks and responds in one of the following ways:


Prompt:
Explain this text code:

Reason:         Terminated
Message:        Pod was terminated in response to imminent node shutdown.

Response: This text code 11.1 - Node Shutdowns | To provide more flexibility during graceful node shutdown around the ordering of pods during shutdown, graceful node shutdown honors the PriorityClass for Pods, provided that you enabled this feature

Context: 11.1 - Node Shutdowns | To provide more flexibility during graceful node shutdown around the ordering of pods during shutdown, graceful node shutdown honors the PriorityClass for Pods, provided that you enabled this feature


Prompt:
Complete this yaml code:

shutdownGracePeriodByPodPriority:
  - priority: 100000
    shutdownGracePeriodSeconds: 10
  - priority: 10000

Response:
    shutdownGracePeriodSeconds: 180
  - priority: 1000
    shutdownGracePeriodSeconds: 120
  - priority: 0
    shutdownGracePeriodSeconds: 60

Context: 11.1 - Node Shutdowns | The above table implies that any pod with priority value >= 100000 will get just 10 seconds to shut down, any pod with value >= 10000 and < 100000 will get 180 seconds to shut down, any pod with value


Prompt:
Explain this yaml code:

shutdownGracePeriodByPodPriority:
  - priority: 100000
    shutdownGracePeriodSeconds: 10
  - priority: 10000
    shutdownGracePeriodSeconds: 180
  - priority: 1000
    shutdownGracePeriodSeconds: 120
  - priority: 0
    shutdownGracePeriodSeconds: 60

Response: This yaml code 11.1 - Node Shutdowns | The above table implies that any pod with priority value >= 100000 will get just 10 seconds to shut down, any pod with value >= 10000 and < 100000 will get 180 seconds to shut down, any pod with value


Prompt:
Complete this text code:

NAME    CPU(cores)   CPU(%)   MEMORY(bytes)   MEMORY(%)   SWAP(bytes)    SWAP(%)
node1   1m           10%      2Mi             10%         1Mi            0%

Response:
node2   5m           10%      6Mi             10%         2Mi            0%
node3   3m           10%      4Mi             10%         <unknown>      <unknown>

Context: 11.2 - Swap memory management | In order to receive information about swap usage by pods, kubectl top nodes --show-swap can be used:


Prompt:
Explain this text code:

NAME    CPU(cores)   CPU(%)   MEMORY(bytes)   MEMORY(%)   SWAP(bytes)    SWAP(%)
node1   1m           10%      2Mi             10%         1Mi            0%
node2   5m           10%      6Mi             10%         2Mi            0%
node3   3m           10%      4Mi             10%         <unknown>      <unknown>

Response: This text code 11.2 - Swap memory management | In order to receive information about swap usage by pods, kubectl top nodes --show-swap can be used:


Prompt:
Complete this text code:

NAME                                      CPU(cores)   MEMORY(bytes)   SWAP(bytes)
coredns-58d5bc5cdb-5nbk4                  2m           19Mi            0Mi
coredns-58d5bc5cdb-jsh26                  3m           37Mi            0Mi
etcd-node01                               51m          143Mi           5Mi
kube-apiserver-node01                     98m          824Mi           16Mi

Response:
kube-controller-manager-node01            20m          135Mi           9Mi
kube-proxy-ffgs2                          1m           24Mi            0Mi
kube-proxy-fhvwx                          1m           39Mi            0Mi
kube-scheduler-node01                     13m          69Mi            0Mi
metrics-server-8598789fdb-d2kcj           5m           26Mi            0Mi

Context: 11.2 - Swap memory management | A new node status field is now added, node.status.nodeInfo.swap.capacity, to report the swap capacity of a node.


Prompt:
Explain this text code:

NAME                                      CPU(cores)   MEMORY(bytes)   SWAP(bytes)
coredns-58d5bc5cdb-5nbk4                  2m           19Mi            0Mi
coredns-58d5bc5cdb-jsh26                  3m           37Mi            0Mi
etcd-node01                               51m          143Mi           5Mi
kube-apiserver-node01                     98m          824Mi           16Mi
kube-controller-manager-node01            20m          135Mi           9Mi
kube-proxy-ffgs2                          1m           24Mi            0Mi
kube-proxy-fhvwx                          1m           39Mi            0Mi
kube-scheduler-node01                     13m          69Mi            0Mi
metrics-server-8598789fdb-d2kcj           5m           26Mi            0Mi

Response: This text code 11.2 - Swap memory management | A new node status field is now added, node.status.nodeInfo.swap.capacity, to report the swap capacity of a node.


Prompt:
Explain this shell code:

kubectl get nodes -o go-template='{{range .items}}{{.metadata.name}}: {{if .status.nodeInfo.swap.capacity}}{{.status.nodeInfo.swap.capacity}}{{else}}<unknown>{{end}}{{"\n"}}{{end}}'

Response: This shell code 11.2 - Swap memory management | This will result in an output similar to:

Context: 11.2 - Swap memory management | This will result in an output similar to:


Prompt:
Explain this text code:

node1: 21474836480
node2: 42949664768
node3: <unknown>

Response: This text code 11.2 - Swap memory management | Node Feature Discovery is a Kubernetes addon for detecting hardware features and configuration. It can be utilized to discover which nodes are provisioned with swap.

Context: 11.2 - Swap memory management | Node Feature Discovery is a Kubernetes addon for detecting hardware features and configuration. It can be utilized to discover which nodes are provisioned with swap.


Prompt:
Explain this shell code:

kubectl get nodes -o jsonpath='{range .items[?(@.metadata.labels.feature\.node\.kubernetes\.io/memory-swap)]}{.metadata.name}{"\t"}{.metadata.labels.feature\.node\.kubernetes\.io/memory-swap}{"\n"}{end}'

Response: This shell code 11.2 - Swap memory management | This will result in an output similar to:


Prompt:
Explain this text code:

k8s-worker1: true
k8s-worker2: true
k8s-worker3: false

Response: This text code 11.2 - Swap memory management | In this example, swap is provisioned on nodes k8s-worker1 and k8s-worker2, but not on k8s-worker3.

Context: 11.2 - Swap memory management | In this example, swap is provisioned on nodes k8s-worker1 and k8s-worker2, but not on k8s-worker3.


Prompt:
Explain this promql code:

sum(rate(workqueue_adds_total{name="resource_claim"}[5m]))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | If you are experiencing low Workqueue Add Rate, high Workqueue Depth, and/or high Workqueue Work Duration, this suggests the controller isn't performing optimally. Consider tuning parameters like QPS,

Context: 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | If you are experiencing low Workqueue Add Rate, high Workqueue Depth, and/or high Workqueue Work Duration, this suggests the controller isn't performing optimally. Consider tuning parameters like QPS,


Prompt:
Explain this promql code:

sum(workqueue_depth{endpoint="kube-controller-manager",
name="resource_claim"})

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | If you are experiencing low Workqueue Add Rate, high Workqueue Depth, and/or high Workqueue Work Duration, this suggests the controller isn't performing optimally. Consider tuning parameters like QPS,


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(rate(workqueue_work_duration_seconds_bucket{name="resource_claim"}[5m]))
by (le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | If you are experiencing low Workqueue Add Rate, high Workqueue Depth, and/or high Workqueue Work Duration, this suggests the controller isn't performing optimally. Consider tuning parameters like QPS,


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(increase(scheduler_pod_scheduling_sli_duration_seconds_bucket[5m])) by
(le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | When a Pod bound to a node must have a ResourceClaim satisfied, kubelet calls the NodePrepareResources and NodeUnprepareResources methods of the DRA driver. You can observe this behavior from the kube

Context: 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | When a Pod bound to a node must have a ResourceClaim satisfied, kubelet calls the NodePrepareResources and NodeUnprepareResources methods of the DRA driver. You can observe this behavior from the kube


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(increase(scheduler_scheduling_algorithm_duration_seconds_bucket[5m])) by
(le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | When a Pod bound to a node must have a ResourceClaim satisfied, kubelet calls the NodePrepareResources and NodeUnprepareResources methods of the DRA driver. You can observe this behavior from the kube


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(rate(dra_operations_duration_seconds_bucket{operation_name="PrepareResources"}[5m]))
by (le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | DRA drivers implement the kubeletplugin package interface which surfaces its own metric for the underlying gRPC operation NodePrepareResources and NodeUnprepareResources. You can observe this behavior

Context: 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | DRA drivers implement the kubeletplugin package interface which surfaces its own metric for the underlying gRPC operation NodePrepareResources and NodeUnprepareResources. You can observe this behavior


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(rate(dra_operations_duration_seconds_bucket{operation_name="UnprepareResources"}[5m]))
by (le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | DRA drivers implement the kubeletplugin package interface which surfaces its own metric for the underlying gRPC operation NodePrepareResources and NodeUnprepareResources. You can observe this behavior


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(rate(dra_grpc_operations_duration_seconds_bucket{method_name=~".*NodePrepareResources"}[5m]))
by (le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | Application logs can help you understand what is happening inside your application. The logs are particularly useful for debugging problems and monitoring cluster activity. Most modern applications ha

Context: 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | Application logs can help you understand what is happening inside your application. The logs are particularly useful for debugging problems and monitoring cluster activity. Most modern applications ha


Prompt:
Explain this promql code:

histogram_quantile(0.99,
sum(rate(dra_grpc_operations_duration_seconds_bucket{method_name=~".*NodeUnprepareResources"}[5m]))
by (le))

Response: This promql code 11.7 - Good practices for Dynamic Resource Allocation as a Cluster Admin | Application logs can help you understand what is happening inside your application. The logs are particularly useful for debugging problems and monitoring cluster activity. Most modern applications ha


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:

Response:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; i=$((i+1)); sleep 1; done']

Context: 11.8 - Logging Architecture | To run this pod, use the following command:


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; i=$((i+1)); sleep 1; done']

Response: This yaml code 11.8 - Logging Architecture | To run this pod, use the following command:


Prompt:
Explain this shell code:

kubectl apply -f https://k8s.io/examples/debug/counter-pod.yaml

Response: This shell code 11.8 - Logging Architecture | The output is:

Context: 11.8 - Logging Architecture | The output is:


Prompt:
Explain this console code:

0: Fri Apr  1 11:42:23 UTC 2022
1: Fri Apr  1 11:42:24 UTC 2022
2: Fri Apr  1 11:42:25 UTC 2022

Response: This console code 11.8 - Logging Architecture | You can use kubectl logs --previous to retrieve logs from a previous instantiation of a container. If your pod has multiple containers, specify which container's logs you want to access by appending a

Context: 11.8 - Logging Architecture | You can use kubectl logs --previous to retrieve logs from a previous instantiation of a container. If your pod has multiple containers, specify which container's logs you want to access by appending a


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter-err
spec:

Response:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; echo "$i: err" >&2 ; i=$((i+1)); sleep 1; done']


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter-err
spec:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; echo "$i: err" >&2 ; i=$((i+1)); sleep 1; done']

Response: This yaml code 11.8 - Logging Architecture | To run this pod, use the following command:


Prompt:
Explain this shell code:

kubectl apply -f https://k8s.io/examples/debug/counter-pod-err.yaml

Response: This shell code 11.8 - Logging Architecture | To fetch only the stderr log stream, you can run:

Context: 11.8 - Logging Architecture | To fetch only the stderr log stream, you can run:


Prompt:
Explain this shell code:

kubectl get --raw "/api/v1/namespaces/default/pods/counter-err/log?stream=Stderr"

Response: This shell code 11.8 - Logging Architecture | See the kubectl logs documentation for more details.

Context: 11.8 - Logging Architecture | See the kubectl logs documentation for more details.


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;

Response:
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

Context: 11.8 - Logging Architecture | It is not recommended to write log entries with different formats to the same log stream, even if you managed to redirect both components to the stdout stream of the container. Instead, you can create


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

Response: This yaml code 11.8 - Logging Architecture | It is not recommended to write log entries with different formats to the same log stream, even if you managed to redirect both components to the stdout stream of the container. Instead, you can create


Prompt:
Explain this text code:

admin/logging/two-files-counter-pod-streaming-sidecar.yaml

Response: This text code 11.8 - Logging Architecture | Now when you run this pod, you can access each log stream separately by running the following commands:

Context: 11.8 - Logging Architecture | Now when you run this pod, you can access each log stream separately by running the following commands:


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;

Response:
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-1
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/1.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-2
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/2.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-1
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/1.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-2
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/2.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

Response: This yaml code 11.8 - Logging Architecture | Now when you run this pod, you can access each log stream separately by running the following commands:


Prompt:
Complete this console code:

0: Fri Apr  1 11:42:26 UTC 2022
1: Fri Apr  1 11:42:27 UTC 2022

Response:
2: Fri Apr  1 11:42:28 UTC 2022
...

Context: 11.8 - Logging Architecture | The output is similar to:


Prompt:
Explain this console code:

0: Fri Apr  1 11:42:26 UTC 2022
1: Fri Apr  1 11:42:27 UTC 2022
2: Fri Apr  1 11:42:28 UTC 2022
...

Response: This console code 11.8 - Logging Architecture | The output is similar to:


Prompt:
Complete this console code:

Fri Apr  1 11:42:29 UTC 2022 INFO 0
Fri Apr  1 11:42:30 UTC 2022 INFO 0

Response:
Fri Apr  1 11:42:31 UTC 2022 INFO 0
...

Context: 11.8 - Logging Architecture | If you installed a node-level agent in your cluster, that agent picks up those log streams automatically without any further configuration. If you like, you can configure the agent to parse log lines


Prompt:
Explain this console code:

Fri Apr  1 11:42:29 UTC 2022 INFO 0
Fri Apr  1 11:42:30 UTC 2022 INFO 0
Fri Apr  1 11:42:31 UTC 2022 INFO 0
...

Response: This console code 11.8 - Logging Architecture | If you installed a node-level agent in your cluster, that agent picks up those log streams automatically without any further configuration. If you like, you can configure the agent to parse log lines


Prompt:
Complete this yaml code:

apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-config
data:
  fluentd.conf: |
    <source>
      type tail
      format none
      path /var/log/1.log
      pos_file /var/log/1.log.pos
      tag count.format1

Response:
    </source>

    <source>
      type tail
      format none
      path /var/log/2.log
      pos_file /var/log/2.log.pos
      tag count.format2
    </source>

    <match **>
      type google_cloud
    </match>

Context: 11.8 - Logging Architecture | The second manifest describes a pod that has a sidecar container running fluentd. The pod mounts a volume where fluentd can pick up its configuration data.


Prompt:
Explain this yaml code:

apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-config
data:
  fluentd.conf: |
    <source>
      type tail
      format none
      path /var/log/1.log
      pos_file /var/log/1.log.pos
      tag count.format1
    </source>

    <source>
      type tail
      format none
      path /var/log/2.log
      pos_file /var/log/2.log.pos
      tag count.format2
    </source>

    <match **>
      type google_cloud
    </match>

Response: This yaml code 11.8 - Logging Architecture | The second manifest describes a pod that has a sidecar container running fluentd. The pod mounts a volume where fluentd can pick up its configuration data.


Prompt:
Explain this text code:

admin/logging/two-files-counter-pod-agent-sidecar.yaml

Response: This text code 11.8 - Logging Architecture |

Context: 11.8 - Logging Architecture |


Prompt:
Complete this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;

Response:
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-agent
    image: registry.k8s.io/fluentd-gcp:1.30
    env:
    - name: FLUENTD_ARGS
      value: -c /etc/fluentd-config/fluentd.conf
    volumeMounts:
    - name: varlog
      mountPath: /var/log
    - name: config-volume
      mountPath: /etc/fluentd-config
  volumes:
  - name: varlog
    emptyDir: {}
  - name: config-volume
    configMap:
      name: fluentd-config

Context: 11.8 - Logging Architecture |


Prompt:
Explain this yaml code:

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-agent
    image: registry.k8s.io/fluentd-gcp:1.30
    env:
    - name: FLUENTD_ARGS
      value: -c /etc/fluentd-config/fluentd.conf
    volumeMounts:
    - name: varlog
      mountPath: /var/log
    - name: config-volume
      mountPath: /etc/fluentd-config
  volumes:
  - name: varlog
    emptyDir: {}
  - name: config-volume
    configMap:
      name: fluentd-config

Response: This yaml code 11.8 - Logging Architecture |

Context: 11.8 - Logging Architecture |


Prompt:
Complete this yaml code:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus

Response:
rules:
  - nonResourceURLs:
      - "/metrics"
    verbs:
      - get

Context: 11.10 - Metrics For Kubernetes System Components | Alpha metric → Stable metric → Deprecated metric → Hidden metric → Deleted metric


Prompt:
Explain this yaml code:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - nonResourceURLs:
      - "/metrics"
    verbs:
      - get

Response: This yaml code 11.10 - Metrics For Kubernetes System Components | Alpha metric → Stable metric → Deprecated metric → Hidden metric → Deleted metric


Prompt:
Explain this text code:

# HELP some_counter this counts things
# TYPE some_counter counter
some_counter 0

Response: This text code 11.10 - Metrics For Kubernetes System Components | After deprecation

Context: 11.10 - Metrics For Kubernetes System Components | After deprecation


Prompt:
Explain this text code:

# HELP some_counter (Deprecated since 1.15.0) this counts things
# TYPE some_counter counter
some_counter 0

Response: This text code 11.10 - Metrics For Kubernetes System Components | Hidden metrics are no longer published for scraping, but are still available for use. To use a hidden metric, please refer to the Show hidden metrics section.

Context: 11.10 - Metrics For Kubernetes System Components | Hidden metrics are no longer published for scraping, but are still available for use. To use a hidden metric, please refer to the Show hidden metrics section.


Prompt:
Complete this text code:

cloudprovider_gce_api_request_duration_seconds { request = "instance_list"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_insert"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_delete"}

Response:
cloudprovider_gce_api_request_duration_seconds { request = "attach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "detach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "list_disk"}

Context: 11.10 - Metrics For Kubernetes System Components | The scheduler exposes optional metrics that reports the requested resources and the desired limits of all running pods. These metrics can be used to build capacity planning dashboards, assess current


Prompt:
Explain this text code:

cloudprovider_gce_api_request_duration_seconds { request = "instance_list"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_insert"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_delete"}
cloudprovider_gce_api_request_duration_seconds { request = "attach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "detach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "list_disk"}

Response: This text code 11.10 - Metrics For Kubernetes System Components | The scheduler exposes optional metrics that reports the requested resources and the desired limits of all running pods. These metrics can be used to build capacity planning dashboards, assess current


Prompt:
Complete this text code:

container_pressure_cpu_stalled_seconds_total
container_pressure_cpu_waiting_seconds_total
container_pressure_memory_stalled_seconds_total

Response:
container_pressure_memory_waiting_seconds_total
container_pressure_io_stalled_seconds_total
container_pressure_io_waiting_seconds_total

Context: 11.10 - Metrics For Kubernetes System Components | This feature is enabled by default, by setting the KubeletPSI feature gate. The information is also exposed in the Summary API.


Prompt:
Explain this text code:

container_pressure_cpu_stalled_seconds_total
container_pressure_cpu_waiting_seconds_total
container_pressure_memory_stalled_seconds_total
container_pressure_memory_waiting_seconds_total
container_pressure_io_stalled_seconds_total
container_pressure_io_waiting_seconds_total

Response: This text code 11.10 - Metrics For Kubernetes System Components | This feature is enabled by default, by setting the KubeletPSI feature gate. The information is also exposed in the Summary API.


Prompt:
Explain this text code:

--allow-metric-labels <metric_name>,<label_name>='<allow_value1>, <allow_value2>...', <metric_name2>,<label_name>='<allow_value1>, <allow_value2>...', ...

Response: This text code 11.10 - Metrics For Kubernetes System Components | Here is an example:

Context: 11.10 - Metrics For Kubernetes System Components | Here is an example:


Prompt:
Explain this none code:

--allow-metric-labels number_count_metric,odd_number='1,3,5', number_count_metric,even_number='2,4,6', date_gauge_metric,weekend='Saturday,Sunday'

Response: This none code 11.10 - Metrics For Kubernetes System Components | In addition to specifying this from the CLI, this can also be done within a configuration file. You can specify the path to that configuration file using the --allow-metric-labels-manifest command lin

Context: 11.10 - Metrics For Kubernetes System Components | In addition to specifying this from the CLI, this can also be done within a configuration file. You can specify the path to that configuration file using the --allow-metric-labels-manifest command lin


Prompt:
Explain this yaml code:

"metric1,label2": "v1,v2,v3"
"metric2,label1": "v1,v2,v3"

Response: This yaml code 11.10 - Metrics For Kubernetes System Components | Additionally, the cardinality_enforcement_unexpected_categorizations_total meta-metric records the count of unexpected categorizations during cardinality enforcement, that is, whenever a label value i

Context: 11.10 - Metrics For Kubernetes System Components | Additionally, the cardinality_enforcement_unexpected_categorizations_total meta-metric records the count of unexpected categorizations during cardinality enforcement, that is, whenever a label value i


Prompt:
Explain this text code:

cardinality_enforcement_unexpected_categorizations_total

Response: This text code 11.10 - Metrics For Kubernetes System Components | The state of Kubernetes objects in the Kubernetes API can be exposed as metrics. An add-on agent called kube-state-metrics can connect to the Kubernetes API server and expose a HTTP endpoint with metr

Context: 11.10 - Metrics For Kubernetes System Components | The state of Kubernetes objects in the Kubernetes API can be exposed as metrics. An add-on agent called kube-state-metrics can connect to the Kubernetes API server and expose a HTTP endpoint with metr


Prompt:
Explain this text code:

count(kube_pod_status_ready{condition="false"}) by (namespace, pod)

Response: This text code 11.11 - Metrics for Kubernetes Object States | Metrics generated from kube-state-metrics also allow for alerting on issues in the cluster.

Context: 11.11 - Metrics for Kubernetes Object States | Metrics generated from kube-state-metrics also allow for alerting on issues in the cluster.


Prompt:
Complete this yaml code:

groups:
- name: Pod state
  rules:
  - alert: PodsBlockedInTerminatingState
    expr: count(kube_pod_deletion_timestamp) by (namespace, pod) * count(kube_pod_status_reason{reason="NodeLost"} == 0) by (namespace, pod) > 0

Response:
    for: 5m
    labels:
      severity: page
    annotations:
      summary: Pod {{$labels.namespace}}/{{$labels.pod}} blocked in Terminating state.

Context: 11.11 - Metrics for Kubernetes Object States | System component logs record events happening in cluster, which can be very useful for debugging. You can configure log verbosity to see more or less detail. Logs can be as coarse-grained as showing e


Prompt:
Explain this yaml code:

groups:
- name: Pod state
  rules:
  - alert: PodsBlockedInTerminatingState
    expr: count(kube_pod_deletion_timestamp) by (namespace, pod) * count(kube_pod_status_reason{reason="NodeLost"} == 0) by (namespace, pod) > 0
    for: 5m
    labels:
      severity: page
    annotations:
      summary: Pod {{$labels.namespace}}/{{$labels.pod}} blocked in Terminating state.

Response: This yaml code 11.11 - Metrics for Kubernetes Object States | System component logs record events happening in cluster, which can be very useful for debugging. You can configure log verbosity to see more or less detail. Logs can be as coarse-grained as showing e


Prompt:
Explain this text code:

kube-log-runner -log-file=/tmp/log -redirect-stderr=false

Response: This text code 11.12 - System Logs | An example of the traditional klog native format:

Context: 11.12 - System Logs | An example of the traditional klog native format:


Prompt:
Explain this text code:

I1025 00:15:15.525108       1 httplog.go:79] GET /api/v1/namespaces/kube-system/pods/metrics-server-v0.3.1-57c75779f-9p8wg: (1.512ms) 200 [pod_nanny/v0.0.0 (linux/amd64) kubernetes/$Format 10.56.1.19:51756]

Response: This text code 11.12 - System Logs | The message string may contain line breaks:

Context: 11.12 - System Logs | The message string may contain line breaks:


Prompt:
Explain this text code:

I1025 00:15:15.525108       1 example.go:79] This is a message
which has a line break.

Response: This text code 11.12 - System Logs | Migration to structured log messages is an ongoing process. Not all log messages are structured in this version. When parsing log files, you must also handle unstructured log messages.

Context: 11.12 - System Logs | Migration to structured log messages is an ongoing process. Not all log messages are structured in this version. When parsing log files, you must also handle unstructured log messages.


Prompt:
Explain this text code:

<klog header> "<message>" <key1>="<value1>" <key2>="<value2>" ...

Response: This text code 11.12 - System Logs | Example:

Context: 11.12 - System Logs | Example:


Prompt:
Explain this text code:

I1025 00:15:15.525108       1 controller_utils.go:116] "Pod status updated" pod="kube-system/kubedns" status="ready"

Response: This text code 11.12 - System Logs | Strings are quoted. Other values are formatted with %+v, which may cause log messages to continue on the next line depending on the data.

Context: 11.12 - System Logs | Strings are quoted. Other values are formatted with %+v, which may cause log messages to continue on the next line depending on the data.


Prompt:
Explain this text code:

I1025 00:15:15.525108       1 example.go:116] "Example" data="This is text with a line break\nand \"quotation marks\"." someInt=1 someFloat=0.1 someStruct={StringField: First line,
second line.}

Response: This text code 11.12 - System Logs | Contextual logging builds on top of structured logging. It is primarily about how developers use logging calls: code based on that concept is more flexible and supports additional use cases as describ

Context: 11.12 - System Logs | Contextual logging builds on top of structured logging. It is primarily about how developers use logging calls: code based on that concept is more flexible and supports additional use cases as describ


Prompt:
Complete this console code:

$ cd $GOPATH/src/k8s.io/kubernetes/staging/src/k8s.io/component-base/logs/example/cmd/
$ go run . --help
...
      --feature-gates mapStringBool  A set of key=value pairs that describe feature gates for alpha/experimental features. Options are:
                                     AllAlpha=true|false (ALPHA - default=false)

Response:
                                     AllBeta=true|false (BETA - default=false)
                                     ContextualLogging=true|false (BETA - default=true)
$ go run . --feature-gates ContextualLogging=true
...
I0222 15:13:31.645988  197901 example.go:54] "runtime" logger="example.myname" foo="bar" duration="1m0s"
I0222 15:13:31.646007  197901 example.go:55] "another runtime" logger="example" foo="bar" duration="1h0m0s" duration="1m0s"

Context: 11.12 - System Logs | The logger key and foo="bar" were added by the caller of the function which logs the runtime message and duration="1m0s" value, without having to modify that function.


Prompt:
Explain this console code:

$ cd $GOPATH/src/k8s.io/kubernetes/staging/src/k8s.io/component-base/logs/example/cmd/
$ go run . --help
...
      --feature-gates mapStringBool  A set of key=value pairs that describe feature gates for alpha/experimental features. Options are:
                                     AllAlpha=true|false (ALPHA - default=false)
                                     AllBeta=true|false (BETA - default=false)
                                     ContextualLogging=true|false (BETA - default=true)
$ go run . --feature-gates ContextualLogging=true
...
I0222 15:13:31.645988  197901 example.go:54] "runtime" logger="example.myname" foo="bar" duration="1m0s"
I0222 15:13:31.646007  197901 example.go:55] "another runtime" logger="example" foo="bar" duration="1h0m0s" duration="1m0s"

Response: This console code 11.12 - System Logs | The logger key and foo="bar" were added by the caller of the function which logs the runtime message and duration="1m0s" value, without having to modify that function.


Prompt:
Complete this console code:

$ go run . --feature-gates ContextualLogging=false
...

Response:
I0222 15:14:40.497333  198174 example.go:54] "runtime" duration="1m0s"
I0222 15:14:40.497346  198174 example.go:55] "another runtime" duration="1h0m0s" duration="1m0s"

Context: 11.12 - System Logs | JSON output does not support many standard klog flags. For list of unsupported klog flags, see the Command line tool reference.


Prompt:
Explain this console code:

$ go run . --feature-gates ContextualLogging=false
...
I0222 15:14:40.497333  198174 example.go:54] "runtime" duration="1m0s"
I0222 15:14:40.497346  198174 example.go:55] "another runtime" duration="1h0m0s" duration="1m0s"

Response: This console code 11.12 - System Logs | JSON output does not support many standard klog flags. For list of unsupported klog flags, see the Command line tool reference.


Prompt:
Complete this json code:

{
   "ts": 1580306777.04728,
   "v": 4,
   "msg": "Pod status updated",
   "pod":{

Response:
      "name": "nginx-1",
      "namespace": "default"
   },
   "status": "ready"
}

Context: 11.12 - System Logs | Keys with special meaning:


Prompt:
Explain this json code:

{
   "ts": 1580306777.04728,
   "v": 4,
   "msg": "Pod status updated",
   "pod":{
      "name": "nginx-1",
      "namespace": "default"
   },
   "status": "ready"
}

Response: This json code 11.12 - System Logs | Keys with special meaning:


Prompt:
Explain this shell code:

# Fetch kubelet logs from a node named node-1.example
kubectl get --raw "/api/v1/nodes/node-1.example/proxy/logs/?query=kubelet"

Response: This shell code 11.12 - System Logs | You can also fetch files, provided that the files are in a directory that the kubelet allows for log fetches. For example, you can fetch a log from /var/log on a Linux node:

Context: 11.12 - System Logs | You can also fetch files, provided that the files are in a directory that the kubelet allows for log fetches. For example, you can fetch a log from /var/log on a Linux node:


Prompt:
Explain this shell code:

kubectl get --raw "/api/v1/nodes/<insert-node-name-here>/proxy/logs/?query=/<insert-log-file-name-here>"

Response: This shell code 11.12 - System Logs | The kubelet uses heuristics to retrieve logs. This helps if you are not aware whether a given system service is writing logs to the operating system's native logger like journald or to a log file in /

Context: 11.12 - System Logs | The kubelet uses heuristics to retrieve logs. This helps if you are not aware whether a given system service is writing logs to the operating system's native logger like journald or to a log file in /


Prompt:
Explain this shell code:

# Fetch kubelet logs from a node named node-1.example that have the word "error"
kubectl get --raw "/api/v1/nodes/node-1.example/proxy/logs/?query=kubelet&pattern=error"

Response: This shell code 11.12 - System Logs | System component traces record the latency of and relationships between operations in the cluster.

Context: 11.12 - System Logs | System component traces record the latency of and relationships between operations in the cluster.


Prompt:
Complete this yaml code:

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  # Replace this exporter with the exporter for your backend
  exporters:

Response:
    debug:
      verbosity: detailed
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [debug]

Context: 11.13 - Traces For Kubernetes System Components | To directly emit traces to a backend without utilizing a collector, specify the endpoint field in the Kubernetes tracing configuration file with the desired trace backend address. This method negates


Prompt:
Explain this yaml code:

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  # Replace this exporter with the exporter for your backend
  exporters:
    debug:
      verbosity: detailed
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [debug]

Response: This yaml code 11.13 - Traces For Kubernetes System Components | To directly emit traces to a backend without utilizing a collector, specify the endpoint field in the Kubernetes tracing configuration file with the desired trace backend address. This method negates


Prompt:
Complete this yaml code:

apiVersion: apiserver.config.k8s.io/v1
kind: TracingConfiguration

Response:
# default value
#endpoint: localhost:4317
samplingRatePerMillion: 100

Context: 11.13 - Traces For Kubernetes System Components | For more information about the TracingConfiguration struct, see API server config API (v1).


Prompt:
Explain this yaml code:

apiVersion: apiserver.config.k8s.io/v1
kind: TracingConfiguration
# default value
#endpoint: localhost:4317
samplingRatePerMillion: 100

Response: This yaml code 11.13 - Traces For Kubernetes System Components | For more information about the TracingConfiguration struct, see API server config API (v1).


Prompt:
Complete this yaml code:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
tracing:

Response:
  # default value
  #endpoint: localhost:4317
  samplingRatePerMillion: 100

Context: 11.13 - Traces For Kubernetes System Components | If the samplingRatePerMillion is set to one million (1000000), then every span will be sent to the exporter.


Prompt:
Explain this yaml code:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
tracing:
  # default value
  #endpoint: localhost:4317
  samplingRatePerMillion: 100

Response: This yaml code 11.13 - Traces For Kubernetes System Components | If the samplingRatePerMillion is set to one million (1000000), then every span will be sent to the exporter.


Prompt:
Explain this shell code:

kube-apiserver \
--runtime-config=flowcontrol.apiserver.k8s.io/v1beta3=false \
 # …and other flags as usual

Response: This shell code 11.15 - API Priority and Fairness | The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature.

Context: 11.15 - API Priority and Fairness | The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature.


Prompt:
Complete this yaml code:

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
metadata:
  name: health-for-strangers
spec:
  matchingPrecedence: 1000
  priorityLevelConfiguration:
    name: exempt
  rules:
    - nonResourceRules:

Response:
      - nonResourceURLs:
          - "/healthz"
          - "/livez"
          - "/readyz"
        verbs:
          - "*"
      subjects:
        - kind: Group
          group:
            name: "system:unauthenticated"

Context: 11.15 - API Priority and Fairness | When you enable the API Priority and Fairness feature, the kube-apiserver exports additional metrics. Monitoring these can help you determine whether your configuration is inappropriately throttling i


Prompt:
Explain this yaml code:

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
metadata:
  name: health-for-strangers
spec:
  matchingPrecedence: 1000
  priorityLevelConfiguration:
    name: exempt
  rules:
    - nonResourceRules:
      - nonResourceURLs:
          - "/healthz"
          - "/livez"
          - "/readyz"
        verbs:
          - "*"
      subjects:
        - kind: Group
          group:
            name: "system:unauthenticated"

Response: This yaml code 11.15 - API Priority and Fairness | When you enable the API Priority and Fairness feature, the kube-apiserver exports additional metrics. Monitoring these can help you determine whether your configuration is inappropriately throttling i


Prompt:
Explain this text code:

apiserver_flowcontrol_request_wait_duration_seconds

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_nominal_limit_seats is a gauge vector holding each priority level's nominal concurrency limit, computed from the API server's total concurrency limit and the priority level's con

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_nominal_limit_seats is a gauge vector holding each priority level's nominal concurrency limit, computed from the API server's total concurrency limit and the priority level's con


Prompt:
Explain this text code:

apiserver_flowcontrol_read_vs_write_current_requests

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_concurrency_in_use is a gauge vector holding the instantaneous number of occupied seats, broken down by priority_level and flow_schema.

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_concurrency_in_use is a gauge vector holding the instantaneous number of occupied seats, broken down by priority_level and flow_schema.


Prompt:
Explain this text code:

apiserver_flowcontrol_priority_level_request_utilization

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_priority_level_seat_utilization is a histogram vector of observations, made at the end of each nanosecond, of the utilization of a priority level's concurrency limit, broken down

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_priority_level_seat_utilization is a histogram vector of observations, made at the end of each nanosecond, of the utilization of a priority level's concurrency limit, broken down


Prompt:
Explain this text code:

apiserver_flowcontrol_priority_level_seat_utilization

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_queue_length_after_enqueue is a histogram vector of queue lengths for the queues, broken down by priority_level and flow_schema, as sampled by the enqueued requests. Each

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_queue_length_after_enqueue is a histogram vector of queue lengths for the queues, broken down by priority_level and flow_schema, as sampled by the enqueued requests. Each


Prompt:
Explain this text code:

apiserver_flowcontrol_request_queue_length_after_enqueue

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_concurrency_limit is the same as apiserver_flowcontrol_nominal_limit_seats. Before the introduction of concurrency borrowing between priority levels, this was always equa

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_request_concurrency_limit is the same as apiserver_flowcontrol_nominal_limit_seats. Before the introduction of concurrency borrowing between priority levels, this was always equa


Prompt:
Explain this text code:

apiserver_flowcontrol_request_dispatch_no_accommodation_total

Response: This text code 11.15 - API Priority and Fairness | apiserver_flowcontrol_epoch_advance_total is a counter vector of the number of attempts to jump a priority level's progress meter backward to avoid numeric overflow, grouped by priority_level and succ

Context: 11.15 - API Priority and Fairness | apiserver_flowcontrol_epoch_advance_total is a counter vector of the number of attempts to jump a priority level's progress meter backward to avoid numeric overflow, grouped by priority_level and succ


Prompt:
Explain this text code:

priority-and-fairness/list-events-default-service-account.yaml

Response: This text code 11.15 - API Priority and Fairness | Add-ons extend the functionality of Kubernetes.

Context: 11.15 - API Priority and Fairness | Add-ons extend the functionality of Kubernetes.


Prompt:
Complete this yaml code:

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
metadata:
  name: list-events-default-service-account
spec:
  distinguisherMethod:
    type: ByUser
  matchingPrecedence: 8000
  priorityLevelConfiguration:
    name: catch-all
  rules:
    - resourceRules:

Response:
      - apiGroups:
          - '*'
        namespaces:
          - default
        resources:
          - events
        verbs:
          - list
      subjects:
        - kind: ServiceAccount
          serviceAccount:
            name: default
            namespace: default


Prompt:
Explain this yaml code:

apiVersion: flowcontrol.apiserver.k8s.io/v1
kind: FlowSchema
metadata:
  name: list-events-default-service-account
spec:
  distinguisherMethod:
    type: ByUser
  matchingPrecedence: 8000
  priorityLevelConfiguration:
    name: catch-all
  rules:
    - resourceRules:
      - apiGroups:
          - '*'
        namespaces:
          - default
        resources:
          - events
        verbs:
          - list
      subjects:
        - kind: ServiceAccount
          serviceAccount:
            name: default
            namespace: default

Response: This yaml code 11.15 - API Priority and Fairness | Add-ons extend the functionality of Kubernetes.


Prompt:
Explain this text code:

--runtime-config="coordination.k8s.io/v1beta1=true"

Response: This text code 11.17 - Coordinated Leader Election | Provided that you have enabled the CoordinatedLeaderElection feature gate andhave the coordination.k8s.io/v1beta1 API group enabled, compatible control planecomponents automatically use the LeaseCandi

Context: 11.17 - Coordinated Leader Election | Provided that you have enabled the CoordinatedLeaderElection feature gate andhave the coordination.k8s.io/v1beta1 API group enabled, compatible control planecomponents automatically use the LeaseCandi


Prompt:
Explain this text code:

spec.containers[*].securityContext.readOnlyRootFilesystem

Response: This text code 12.1 - Windows containers in Kubernetes | In the above list, wildcards (*) indicate all elements in a list. For example, spec.containers[*].securityContext refers to the SecurityContext object for all containers. If any of these fields is spe

Context: 12.1 - Windows containers in Kubernetes | In the above list, wildcards (*) indicate all elements in a list. For example, spec.containers[*].securityContext refers to the SecurityContext object for all containers. If any of these fields is spe


Prompt:
Explain this text code:

spec.containers[*].securityContext.allowPrivilegeEscalation

Response: This text code 12.1 - Windows containers in Kubernetes | In the above list, wildcards (*) indicate all elements in a list. For example, spec.containers[*].securityContext refers to the SecurityContext object for all containers. If any of these fields is spe


Prompt:
Complete this yaml code:

---
apiVersion: v1
kind: Service
metadata:
  name: win-webserver
  labels:
    app: win-webserver
spec:
  ports:
    # the port that this service should serve on
    - port: 80
      targetPort: 80
  selector:
    app: win-webserver
  type: NodePort
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: win-webserver

Response:
  name: win-webserver
spec:
  replicas: 2
  selector:
    matchLabels:
      app: win-webserver
  template:
    metadata:
      labels:
        app: win-webserver
      name: win-webserver
    spec:
     containers:
      - name: windowswebserver
        image: mcr.microsoft.com/windows/servercore:ltsc2019
        command:
        - powershell.exe
        - -command
        - "<#code used from https://gist.github.com/19WAS85/5424431#> ; $$listener = New-Object System.Net.HttpListener ; $$listener.Prefixes.Add('http://*:80/') ; $$listener.Start() ; $$callerCounts = @{} ; Write-Host('Listening at http://*:80/') ; while ($$listener.IsListening) { ;$$context = $$listener.GetContext() ;$$requestUrl = $$context.Request.Url ;$$clientIP = $$context.Request.RemoteEndPoint.Address ;$$response = $$context.Response ;Write-Host '' ;Write-Host('> {0}' -f $$requestUrl) ;  ;$$count = 1 ;$$k=$$callerCounts.Get_Item($$clientIP) ;if ($$k -ne $$null) { $$count += $$k } ;$$callerCounts.Set_Item($$clientIP, $$count) ;$$ip=(Get-NetAdapter | Get-NetIpAddress); $$header='<html><body><H1>Windows Container Web Server</H1>' ;$$callerCountsString='' ;$$callerCounts.Keys | % { $$callerCountsString+='<p>IP {0} callerCount {1} ' -f $$ip[1].IPAddress,$$callerCounts.Item($$_) } ;$$footer='</body></html>' ;$$content='{0}{1}{2}' -f $$header,$$callerCountsString,$$footer ;Write-Output $$content ;$$buffer = [System.Text.Encoding]::UTF8.GetBytes($$content) ;$$response.ContentLength64 = $$buffer.Length ;$$response.OutputStream.Write($$buffer, 0, $$buffer.Length) ;$$response.Close() ;$$responseStatus = $$response.StatusCode ;Write-Host('< {0}' -f $$responseStatus)  } ; "
     nodeSelector:
      kubernetes.io/os: windows

Context: 12.2 - Guide for Running Windows Containers in Kubernetes | Check that all nodes are healthy:


Prompt:
Explain this yaml code:

---
apiVersion: v1
kind: Service
metadata:
  name: win-webserver
  labels:
    app: win-webserver
spec:
  ports:
    # the port that this service should serve on
    - port: 80
      targetPort: 80
  selector:
    app: win-webserver
  type: NodePort
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: win-webserver
  name: win-webserver
spec:
  replicas: 2
  selector:
    matchLabels:
      app: win-webserver
  template:
    metadata:
      labels:
        app: win-webserver
      name: win-webserver
    spec:
     containers:
      - name: windowswebserver
        image: mcr.microsoft.com/windows/servercore:ltsc2019
        command:
        - powershell.exe
        - -command
        - "<#code used from https://gist.github.com/19WAS85/5424431#> ; $$listener = New-Object System.Net.HttpListener ; $$listener.Prefixes.Add('http://*:80/') ; $$listener.Start() ; $$callerCounts = @{} ; Write-Host('Listening at http://*:80/') ; while ($$listener.IsListening) { ;$$context = $$listener.GetContext() ;$$requestUrl = $$context.Request.Url ;$$clientIP = $$context.Request.RemoteEndPoint.Address ;$$response = $$context.Response ;Write-Host '' ;Write-Host('> {0}' -f $$requestUrl) ;  ;$$count = 1 ;$$k=$$callerCounts.Get_Item($$clientIP) ;if ($$k -ne $$null) { $$count += $$k } ;$$callerCounts.Set_Item($$clientIP, $$count) ;$$ip=(Get-NetAdapter | Get-NetIpAddress); $$header='
Windows Container Web Server
' ;$$callerCountsString='' ;$$callerCounts.Keys | % { $$callerCountsString+='
IP {0} callerCount {1} ' -f $$ip[1].IPAddress,$$callerCounts.Item($$_) } ;$$footer='' ;$$content='{0}{1}{2}' -f $$header,$$callerCountsString,$$footer ;Write-Output $$content ;$$buffer = [System.Text.Encoding]::UTF8.GetBytes($$content) ;$$response.ContentLength64 = $$buffer.Length ;$$response.OutputStream.Write($$buffer, 0, $$buffer.Length) ;$$response.Close() ;$$responseStatus = $$response.StatusCode ;Write-Host('< {0}' -f $$responseStatus)  } ; "
     nodeSelector:
      kubernetes.io/os: windows

Response: This yaml code 12.2 - Guide for Running Windows Containers in Kubernetes | Check that all nodes are healthy:


Prompt:
Explain this bash code:

kubectl apply -f win-webserver.yaml
kubectl get pods -o wide -w

Response: This bash code 12.2 - Guide for Running Windows Containers in Kubernetes | When the service is deployed correctly both Pods are marked as Ready. To exit the watch command, press Ctrl+C.

Context: 12.2 - Guide for Running Windows Containers in Kubernetes | When the service is deployed correctly both Pods are marked as Ready. To exit the watch command, press Ctrl+C.


Prompt:
Complete this yaml code:

nodeSelector:
    kubernetes.io/os: windows
    node.kubernetes.io/windows-build: '10.0.17763'
tolerations:

Response:
    - key: "os"
      operator: "Equal"
      value: "windows"
      effect: "NoSchedule"

Context: 12.2 - Guide for Running Windows Containers in Kubernetes | The Windows Server version used by each pod must match that of the node. If you want to use multiple Windows Server versions in the same cluster, then you should set additional node labels and nodeSel


Prompt:
Explain this yaml code:

nodeSelector:
    kubernetes.io/os: windows
    node.kubernetes.io/windows-build: '10.0.17763'
tolerations:
    - key: "os"
      operator: "Equal"
      value: "windows"
      effect: "NoSchedule"

Response: This yaml code 12.2 - Guide for Running Windows Containers in Kubernetes | The Windows Server version used by each pod must match that of the node. If you want to use multiple Windows Server versions in the same cluster, then you should set additional node labels and nodeSel


Prompt:
Complete this yaml code:

---
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: windows-2019
handler: example-container-runtime-handler
scheduling:
  nodeSelector:

Response:
    kubernetes.io/os: 'windows'
    kubernetes.io/arch: 'amd64'
    node.kubernetes.io/windows-build: '10.0.17763'
  tolerations:
  - effect: NoSchedule
    key: os
    operator: Equal
    value: "windows"

Context: 12.2 - Guide for Running Windows Containers in Kubernetes | Run kubectl create -f runtimeClasses.yml using as a cluster administrator