1.4. Automatically scaling pods with the horizontal pod autoscaler

As a developer, you can use a horizontal pod autoscaler (HPA) to specify how OpenShift Container Platform should automatically increase or decrease the scale of a replication controller or deployment configuration, based on metrics collected from the pods that belong to that replication controller or deployment configuration.

1.4.1. Understanding horizontal pod autoscalers

You can create a horizontal pod autoscaler to specify the minimum and maximum number of pods you want to run, as well as the CPU utilization or memory utilization your pods should target.

重要

Autoscaling for Memory Utilization is a Technology Preview feature only.

After you create a horizontal pod autoscaler, OpenShift Container Platform begins to query the CPU and/or memory resource metrics on the pods. When these metrics are available, the horizontal pod autoscaler computes the ratio of the current metric utilization with the desired metric utilization, and scales up or down accordingly. The query and scaling occurs at a regular interval, but can take one to two minutes before metrics become available.

For replication controllers, this scaling corresponds directly to the replicas of the replication controller. For deployment configurations, scaling corresponds directly to the replica count of the deployment configuration. Note that autoscaling applies only to the latest deployment in the Complete phase.

OpenShift Container Platform automatically accounts for resources and prevents unnecessary autoscaling during resource spikes, such as during start up. Pods in the unready state have 0 CPU usage when scaling up and the autoscaler ignores the pods when scaling down. Pods without known metrics have 0% CPU usage when scaling up and 100% CPU when scaling down. This allows for more stability during the HPA decision. To use this feature, you must configure readiness checks to determine if a new pod is ready for use.

In order to use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics.

1.4.1.1. Supported metrics

The following metrics are supported by horizontal pod autoscalers:

表 1.1. Metrics
Metric	Description	API version
CPU utilization	Number of CPU cores used. Can be used to calculate a percentage of the pod’s requested CPU.	`autoscaling/v1`, `autoscaling/v2beta2`
Memory utilization	Amount of memory used. Can be used to calculate a percentage of the pod’s requested memory.	`autoscaling/v2beta2`

重要

For memory-based autoscaling, memory usage must increase and decrease proportionally to the replica count. On average:

An increase in replica count must lead to an overall decrease in memory (working set) usage per-pod.
A decrease in replica count must lead to an overall increase in per-pod memory usage.

Use the OpenShift Container Platform web console to check the memory behavior of your application and ensure that your application meets these requirements before using memory-based autoscaling.

1.4.1.2. Scaling policies

The autoscaling/v2beta2 API allows you to add scaling policies to a horizontal pod autoscaler. A scaling policy controls how the OpenShift Container Platform horizontal pod autoscaler (HPA) scales pods. Scaling policies allow you to restrict the rate that HPAs scale pods up or down by setting a specific number or specific percentage to scale in a specified period of time. You can also define a stabilization window, which uses previously computed desired states to control scaling if the metrics are fluctuating. You can create multiple policies for the same scaling direction, and determine which policy is used, based on the amount of change. You can also restrict the scaling by timed iterations. The HPA scales pods during an iteration, then performs scaling, as needed, in further iterations.

Sample HPA object with a scaling policy

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: hpa-resource-metrics-memory
  namespace: default
spec:
  behavior:
    scaleDown: 1
      policies: 2
      - type: Pods 3
        value: 4 4
        periodSeconds: 60 5
      - type: Percent
        value: 10 6
        periodSeconds: 60
      selectPolicy: Min 7
      stabilizationWindowSeconds: 300 8
    scaleUp: 9
      policies:
      - type: Pods
        value: 5 10
        periodSeconds: 70
      - type: Percent
        value: 12 11
        periodSeconds: 80
      selectPolicy: Max
      stabilizationWindowSeconds: 0
...

1: Specifies the direction for the scaling policy, either scaleDown or scaleUp. This example creates a policy for scaling down.
2: Defines the scaling policy.
3: Determines if the policy scales by a specific number of pods or a percentage of pods during each iteration. The default value is pods.
4: Determines the amount of scaling, either the number of pods or percentage of pods, during each iteration. There is no default value for scaling down by number of pods.
5: Determines the length of a scaling iteration. The default value is 15 seconds.
6: The default value for scaling down by percentage is 100%.
7: Determines which policy to use first, if multiple policies are defined. Specify Max to use the policy that allows the highest amount of change, Min to use the policy that allows the lowest amount of change, or Disabled to prevent the HPA from scaling in that policy direction. The default value is Max.
8: Determines the time period the HPA should look back at desired states. The default value is 0.
9: This example creates a policy for scaling up.
10: The amount of scaling up by the number of pods. The default value for scaling up the number of pods is 4%.
11: The amount of scaling up by the percentage of pods. The default value for scaling up by percentage is 100%.

Example policy for scaling down

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: hpa-resource-metrics-memory
  namespace: default
spec:
...
  minReplicas: 20
...
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 4
        periodSeconds: 30
      - type: Percent
        value: 10
        periodSeconds: 60
      selectPolicy: Max
    scaleUp:
      selectPolicy: Disabled

In this example, when the number of pods is greater than 40, the percent-based policy is used for scaling down, as that policy results in a larger change, as required by the selectPolicy.

If there are 80 pod replicas, in the first iteration the HPA reduces the pods by 8, which is 10% of the 80 pods (based on the type: Percent and value: 10 parameters), over one minute (periodSeconds: 60). For the next iteration, the number of pods is 72. The HPA calculates that 10% of the remaining pods is 7.2, which it rounds up to 8 and scales down 8 pods. On each subsequent iteration, the number of pods to be scaled is re-calculated based on the number of remaining pods. When the number of pods falls below 40, the pods-based policy is applied, because the pod-based number is greater than the percent-based number. The HPA reduces 4 pods at a time (type: Pods and value: 4), over 30 seconds (periodSeconds: 30), until there are 20 replicas remaining (minReplicas).

The selectPolicy: Disabled parameter prevents the HPA from scaling up the pods. You can manually scale up by adjusting the number of replicas in the replica set or deployment set, if needed.

If set, you can view the scaling policy by using the oc edit command:

$ oc edit hpa hpa-resource-metrics-memory

Example output

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  annotations:
    autoscaling.alpha.kubernetes.io/behavior:\
'{"ScaleUp":{"StabilizationWindowSeconds":0,"SelectPolicy":"Max","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":15},{"Type":"Percent","Value":100,"PeriodSeconds":15}]},\
"ScaleDown":{"StabilizationWindowSeconds":300,"SelectPolicy":"Min","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":60},{"Type":"Percent","Value":10,"PeriodSeconds":60}]}}'
...

1.4.2. Creating a horizontal pod autoscaler for CPU utilization

You can create a horizontal pod autoscaler (HPA) for an existing DeploymentConfig or ReplicationController object that automatically scales the pods associated with that object in order to maintain the CPU usage you specify.

The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain the specified CPU utilization across all pods.

When autoscaling for CPU utilization, you can use the oc autoscale command and specify the minimum and maximum number of pods you want to run at any given time and the average CPU utilization your pods should target. If you do not specify a minimum, the pods are given default values from the OpenShift Container Platform server. To autoscale for a specific CPU value, create a HorizontalPodAutoscaler object with the target CPU and pod limits.

Prerequisites

In order to use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. You can use the oc describe PodMetrics <pod-name> command to determine if metrics are configured. If metrics are configured, the output appears similar to the following, with Cpu and Memory displayed under Usage.

$ oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal

Example output

Name:         openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Namespace:    openshift-kube-scheduler
Labels:       <none>
Annotations:  <none>
API Version:  metrics.k8s.io/v1beta1
Containers:
  Name:  wait-for-host-port
  Usage:
    Memory:  0
  Name:      scheduler
  Usage:
    Cpu:     8m
    Memory:  45440Ki
Kind:        PodMetrics
Metadata:
  Creation Timestamp:  2019-05-23T18:47:56Z
  Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Timestamp:             2019-05-23T18:47:56Z
Window:                1m0s
Events:                <none>

Procedure

To create a horizontal pod autoscaler for CPU utilization:

Perform one of the following one of the following:
- To scale based on the percent of CPU utilization, create a HorizontalPodAutoscaler object for an existing DeploymentConfig object:
```
$ oc autoscale dc/<dc-name> \1
  --min <number> \2
  --max <number> \3
  --cpu-percent=<percent> 4
```
  1
  Specify the name of the DeploymentConfig object. The object must exist.
  2
  Optionally, specify the minimum number of replicas when scaling down.
  3
  Specify the maximum number of replicas when scaling up.
  4
  Specify the target average CPU utilization over all the pods, represented as a percent of requested CPU. If not specified or negative, a default autoscaling policy is used.
- To scale based on the percent of CPU utilization, create a HorizontalPodAutoscaler object for an existing replication controller:
```
$ oc autoscale rc/<rc-name> 1
  --min <number> \2
  --max <number> \3
  --cpu-percent=<percent> 4
```
  1
  Specify the name of the replication controller. The object must exist.
  2
  Specify the minimum number of replicas when scaling down.
  3
  Specify the maximum number of replicas when scaling up.
  4
  Specify the target average CPU utilization over all the pods, represented as a percent of requested CPU. If not specified or negative, a default autoscaling policy is used.
- To scale for a specific CPU value, create a YAML file similar to the following for an existing DeploymentConfig object or replication controller:
  1. Create a YAML file similar to the following:
    apiVersion: autoscaling/v2beta2 1 kind: HorizontalPodAutoscaler metadata: name: cpu-autoscale 2 namespace: default spec: scaleTargetRef: apiVersion: v1 3 kind: ReplicationController 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: cpu 9 target: type: AverageValue 10 averageValue: 500m 11
    1
    Use the autoscaling/v2beta2 API.
    2
    Specify a name for this horizontal pod autoscaler object.
    3
    Specify the API version of the object to scale:
    For a replication controller, use v1,
    For a DeploymentConfig object, use apps.openshift.io/v1.
    4
    Specify the kind of object to scale, either ReplicationController or DeploymentConfig.
    5
    Specify the name of the object to scale. The object must exist.
    6
    Specify the minimum number of replicas when scaling down.
    7
    Specify the maximum number of replicas when scaling up.
    8
    Use the metrics parameter for memory utilization.
    9
    Specify cpu for CPU utilization.
    10
    Set to AverageValue.
    11
    Set to averageValue with the targeted CPU value.
  2. Create the horizontal pod autoscaler:
    $ oc create -f <file-name>.yaml

Verify that the horizontal pod autoscaler was created:

$ oc get hpa cpu-autoscale

Example output

NAME            REFERENCE                       TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
cpu-autoscale   ReplicationController/example   173m/500m       1         10        1          20m

For example, the following command creates a horizontal pod autoscaler that maintains between 3 and 7 replicas of the pods that are controlled by the image-registry DeploymentConfig object in order to maintain an average CPU utilization of 75% across all pods.

$ oc autoscale dc/image-registry --min 3 --max 7 --cpu-percent=75

Example output

deploymentconfig "image-registry" autoscaled

The command creates a horizontal pod autoscaler with the following definition:

$ oc edit hpa frontend -n openshift-image-registry

Example output

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  creationTimestamp: "2020-02-21T20:19:28Z"
  name: image-registry
  namespace: default
  resourceVersion: "32452"
  selfLink: /apis/autoscaling/v1/namespaces/default/horizontalpodautoscalers/frontend
  uid: 1a934a22-925d-431e-813a-d00461ad7521
spec:
  maxReplicas: 7
  minReplicas: 3
  scaleTargetRef:
    apiVersion: apps.openshift.io/v1
    kind: DeploymentConfig
    name: image-registry
  targetCPUUtilizationPercentage: 75
status:
  currentReplicas: 5
  desiredReplicas: 0

The following example shows autoscaling for the image-registry DeploymentConfig object. The initial deployment requires 3 pods. The HPA object increased that minimum to 5 and will increase the pods up to 7 if CPU usage on the pods reaches 75%:

View the current state of the image-registry deployment:

$ oc get dc image-registry

Example output

NAME             REVISION   DESIRED   CURRENT   TRIGGERED BY
image-registry   1          3         3         config

Autoscale the image-registry DeploymentConfig object:

$ oc autoscale dc/image-registry --min=5 --max=7 --cpu-percent=75

Example output

horizontalpodautoscaler.autoscaling/image-registry autoscaled

View the new state of the deployment:

$ oc get dc image-registry

There are now 5 pods in the deployment:

Example output

NAME             REVISION   DESIRED   CURRENT   TRIGGERED BY
image-registry   1          5         5         config

1.4.3. Creating a horizontal pod autoscaler object for memory utilization

You can create a horizontal pod autoscaler (HPA) for an existing DeploymentConfig object or ReplicationController object that automatically scales the pods associated with that object in order to maintain the average memory utilization you specify, either a direct value or a percentage of requested memory.

The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain the specified memory utilization across all pods.

For memory utilization, you can specify the minimum and maximum number of pods and the average memory utilization your pods should target. If you do not specify a minimum, the pods are given default values from the OpenShift Container Platform server.

重要

Autoscaling for memory utilization is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs), might not be functionally complete, and Red Hat does not recommend to use them for production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information on Red Hat Technology Preview features support scope, see https://access.redhat.com/support/offerings/techpreview/.

Prerequisites

$ oc describe PodMetrics openshift-kube-scheduler-ip-10-0-129-223.compute.internal -n openshift-kube-scheduler

Example output

Name:         openshift-kube-scheduler-ip-10-0-129-223.compute.internal
Namespace:    openshift-kube-scheduler
Labels:       <none>
Annotations:  <none>
API Version:  metrics.k8s.io/v1beta1
Containers:
  Name:  scheduler
  Usage:
    Cpu:     2m
    Memory:  41056Ki
  Name:      wait-for-host-port
  Usage:
    Memory:  0
Kind:        PodMetrics
Metadata:
  Creation Timestamp:  2020-02-14T22:21:14Z
  Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-129-223.compute.internal
Timestamp:             2020-02-14T22:21:14Z
Window:                5m0s
Events:                <none>

Procedure

To create a horizontal pod autoscaler for memory utilization:

Create a YAML file for one of the following:
- To scale for a specific memory value, create a HorizontalPodAutoscaler object similar to the following for an existing DeploymentConfig object or replication controller:
  Example output
```
apiVersion: autoscaling/v2beta2 1
kind: HorizontalPodAutoscaler
metadata:
  name: hpa-resource-metrics-memory 2
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: v1 3
    kind: ReplicationController 4
    name: example 5
  minReplicas: 1 6
  maxReplicas: 10 7
  metrics: 8
  - type: Resource
    resource:
      name: memory 9
      target:
        type: AverageValue 10
        averageValue: 500Mi 11
  behavior: 12
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 4
        periodSeconds: 60
      - type: Percent
        value: 10
        periodSeconds: 60
      selectPolicy: Max
```
  1
  Use the autoscaling/v2beta2 API.
  2
  Specify a name for this horizontal pod autoscaler object.
  3
  Specify the API version of the object to scale:
  For a replication controller, use v1,
  For a DeploymentConfig object, use apps.openshift.io/v1.
  4
  Specify the kind of object to scale, either ReplicationController or DeploymentConfig.
  5
  Specify the name of the object to scale. The object must exist.
  6
  Specify the minimum number of replicas when scaling down.
  7
  Specify the maximum number of replicas when scaling up.
  8
  Use the metrics parameter for memory utilization.
  9
  Specify memory for memory utilization.
  10
  Set the type to AverageValue.
  11
  Specify averageValue and a specific memory value.
  12
  Optional: Specify a scaling policy to control the rate of scaling up or down.
- To scale for a percentage, create a HorizontalPodAutoscaler object similar to the following:
  Example output
```
apiVersion: autoscaling/v2beta2 1
kind: HorizontalPodAutoscaler
metadata:
  name: memory-autoscale 2
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps.openshift.io/v1 3
    kind: DeploymentConfig 4
    name: example 5
  minReplicas: 1 6
  maxReplicas: 10 7
  metrics: 8
  - type: Resource
    resource:
      name: memory 9
      target:
        type: Utilization 10
        averageUtilization: 50 11
  behavior: 12
    scaleUp:
      stabilizationWindowSeconds: 180
      policies:
      - type: Pods
        value: 6
        periodSeconds: 120
      - type: Percent
        value: 10
        periodSeconds: 120
      selectPolicy: Max
```
  1
  Use the autoscaling/v2beta2 API.
  2
  Specify a name for this horizontal pod autoscaler object.
  3
  Specify the API version of the object to scale:
  For a replication controller, use v1,
  For a DeploymentConfig object, use apps.openshift.io/v1.
  4
  Specify the kind of object to scale, either ReplicationController or DeploymentConfig.
  5
  Specify the name of the object to scale. The object must exist.
  6
  Specify the minimum number of replicas when scaling down.
  7
  Specify the maximum number of replicas when scaling up.
  8
  Use the metrics parameter for memory utilization.
  9
  Specify memory for memory utilization.
  10
  Set to Utilization.
  11
  Specify averageUtilization and a target average memory utilization over all the pods, represented as a percent of requested memory. The target pods must have memory requests configured.
  12
  Optional: Specify a scaling policy to control the rate of scaling up or down.

Create the horizontal pod autoscaler:

$ oc create -f <file-name>.yaml

For example:

$ oc create -f hpa.yaml

Example output

horizontalpodautoscaler.autoscaling/hpa-resource-metrics-memory created

Verify that the horizontal pod autoscaler was created:

$ oc get hpa hpa-resource-metrics-memory

Example output

NAME                          REFERENCE                       TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
hpa-resource-metrics-memory   ReplicationController/example   2441216/500Mi   1         10        1          20m

$ oc describe hpa hpa-resource-metrics-memory

Example output

Name:                        hpa-resource-metrics-memory
Namespace:                   default
Labels:                      <none>
Annotations:                 <none>
CreationTimestamp:           Wed, 04 Mar 2020 16:31:37 +0530
Reference:                   ReplicationController/example
Metrics:                     ( current / target )
  resource memory on pods:   2441216 / 500Mi
Min replicas:                1
Max replicas:                10
ReplicationController pods:  1 current / 1 desired
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    ReadyForNewScale    recommended size matches current size
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:
  Type     Reason                   Age                 From                       Message
  ----     ------                   ----                ----                       -------
  Normal   SuccessfulRescale        6m34s               horizontal-pod-autoscaler  New size: 1; reason: All metrics below target

1.4.4. Understanding horizontal pod autoscaler status conditions

You can use the status conditions set to determine whether or not the horizontal pod autoscaler (HPA) is able to scale and whether or not it is currently restricted in any way.

The HPA status conditions are available with the v2beta1 version of the autoscaling API.

The HPA responds with the following status conditions:

The AbleToScale condition indicates whether HPA is able to fetch and update metrics, as well as whether any backoff-related conditions could prevent scaling.
- A True condition indicates scaling is allowed.
- A False condition indicates scaling is not allowed for the reason specified.
The ScalingActive condition indicates whether the HPA is enabled (for example, the replica count of the target is not zero) and is able to calculate desired metrics.
- A True condition indicates metrics is working properly.
- A False condition generally indicates a problem with fetching metrics.

The ScalingLimited condition indicates that the desired scale was capped by the maximum or minimum of the horizontal pod autoscaler.

A True condition indicates that you need to raise or lower the minimum or maximum replica count in order to scale.

A False condition indicates that the requested scaling is allowed.

$ oc describe hpa cm-test

Example output

Name:                           cm-test
Namespace:                      prom
Labels:                         <none>
Annotations:                    <none>
CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
Reference:                      ReplicationController/cm-test
Metrics:                        ( current / target )
  "http_requests" on pods:      66m / 500m
Min replicas:                   1
Max replicas:                   4
ReplicationController pods:     1 current / 1 desired
Conditions: 1
  Type              Status    Reason              Message
  ----              ------    ------              -------
  AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
  ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range
Events:

1: The horizontal pod autoscaler status messages.

The following is an example of a pod that is unable to scale:

Example output

Conditions:
  Type         Status  Reason          Message
  ----         ------  ------          -------
  AbleToScale  False   FailedGetScale  the HPA controller was unable to get the target's current scale: no matches for kind "ReplicationController" in group "apps"
Events:
  Type     Reason          Age               From                       Message
  ----     ------          ----              ----                       -------
  Warning  FailedGetScale  6s (x3 over 36s)  horizontal-pod-autoscaler  no matches for kind "ReplicationController" in group "apps"

The following is an example of a pod that could not obtain the needed metrics for scaling:

Example output

Conditions:
  Type                  Status    Reason                    Message
  ----                  ------    ------                    -------
  AbleToScale           True     SucceededGetScale          the HPA controller was able to get the target's current scale
  ScalingActive         False    FailedGetResourceMetric    the HPA was unable to compute the replica count: unable to get metrics for resource cpu: no metrics returned from heapster

The following is an example of a pod where the requested autoscaling was less than the required minimums:

Example output

Conditions:
  Type              Status    Reason              Message
  ----              ------    ------              -------
  AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
  ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range

1.4.4.1. Viewing horizontal pod autoscaler status conditions

You can view the status conditions set on a pod by the horizontal pod autoscaler (HPA).

注意

The horizontal pod autoscaler status conditions are available with the v2beta1 version of the autoscaling API.

Prerequisites

$ oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal

Example output

Name:         openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Namespace:    openshift-kube-scheduler
Labels:       <none>
Annotations:  <none>
API Version:  metrics.k8s.io/v1beta1
Containers:
  Name:  wait-for-host-port
  Usage:
    Memory:  0
  Name:      scheduler
  Usage:
    Cpu:     8m
    Memory:  45440Ki
Kind:        PodMetrics
Metadata:
  Creation Timestamp:  2019-05-23T18:47:56Z
  Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Timestamp:             2019-05-23T18:47:56Z
Window:                1m0s
Events:                <none>

Procedure

To view the status conditions on a pod, use the following command with the name of the pod:

$ oc describe hpa <pod-name>

For example:

$ oc describe hpa cm-test

The conditions appear in the Conditions field in the output.

Example output

Name:                           cm-test
Namespace:                      prom
Labels:                         <none>
Annotations:                    <none>
CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
Reference:                      ReplicationController/cm-test
Metrics:                        ( current / target )
  "http_requests" on pods:      66m / 500m
Min replicas:                   1
Max replicas:                   4
ReplicationController pods:     1 current / 1 desired
Conditions: 1
  Type              Status    Reason              Message
  ----              ------    ------              -------
  AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
  ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range

1.4.5. Additional resources

For more information on replication controllers and deployment controllers, see Understanding deployments and deployment configs.

1.4. Automatically scaling pods with the horizontal pod autoscaler

1.4.1. Understanding horizontal pod autoscalers

1.4.1.1. Supported metrics

1.4.1.2. Scaling policies

1.4.2. Creating a horizontal pod autoscaler for CPU utilization

1.4.3. Creating a horizontal pod autoscaler object for memory utilization

1.4.4. Understanding horizontal pod autoscaler status conditions

1.4.4.1. Viewing horizontal pod autoscaler status conditions

1.4.5. Additional resources

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