Automatically scaling pods with the horizontal pod autoscaler

As a developer, you can use a horizontal pod autoscaler (HPA) to specify how OKD 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. You can create an HPA for any deployment, deployment config, replica set, replication controller, or stateful set.

For information on scaling pods based on custom metrics, see Automatically scaling pods based on custom metrics.

It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects. For more information on these objects, see Understanding deployments.

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.

After you create a horizontal pod autoscaler, OKD 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.

OKD 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.

To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics.

Supported metrics

The following metrics are supported by horizontal pod autoscalers:

Table 1. Metrics
MetricDescriptionAPI version

CPU utilization

Number of CPU cores used. Can be used to calculate a percentage of the pod’s requested CPU.

autoscaling/v1, autoscaling/v2

Memory utilization

Amount of memory used. Can be used to calculate a percentage of the pod’s requested memory.

autoscaling/v2

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

The following example shows autoscaling for the image-registry Deployment object. The initial deployment requires 3 pods. The HPA object increases the minimum to 5. If CPU usage on the pods reaches 75%, the pods increase to 7:

  1. $ oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75

Example output

  1. horizontalpodautoscaler.autoscaling/image-registry autoscaled

Sample HPA for the image-registry Deployment object with minReplicas set to 3

  1. apiVersion: autoscaling/v1
  2. kind: HorizontalPodAutoscaler
  3. metadata:
  4.   name: image-registry
  5.   namespace: default
  6. spec:
  7.   maxReplicas: 7
  8.   minReplicas: 3
  9.   scaleTargetRef:
  10.     apiVersion: apps/v1
  11.     kind: Deployment
  12.     name: image-registry
  13.   targetCPUUtilizationPercentage: 75
  14. status:
  15.   currentReplicas: 5
  16.   desiredReplicas: 0

  1. View the new state of the deployment:

    1. $ oc get deployment image-registry

    There are now 5 pods in the deployment:

    Example output

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

How does the HPA work?

The horizontal pod autoscaler (HPA) extends the concept of pod auto-scaling. The HPA lets you create and manage a group of load-balanced nodes. The HPA automatically increases or decreases the number of pods when a given CPU or memory threshold is crossed.

workflow

Figure 1. High level workflow of the HPA

The HPA is an API resource in the Kubernetes autoscaling API group. The autoscaler works as a control loop with a default of 15 seconds for the sync period. During this period, the controller manager queries the CPU, memory utilization, or both, against what is defined in the YAML file for the HPA. The controller manager obtains the utilization metrics from the resource metrics API for per-pod resource metrics like CPU or memory, for each pod that is targeted by the HPA.

If a utilization value target is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each pod. The controller then takes the average of utilization across all targeted pods and produces a ratio that is used to scale the number of desired replicas. The HPA is configured to fetch metrics from metrics.k8s.io, which is provided by the metrics server. Because of the dynamic nature of metrics evaluation, the number of replicas can fluctuate during scaling for a group of replicas.

To implement the HPA, all targeted pods must have a resource request set on their containers.

About requests and limits

The scheduler uses the resource request that you specify for containers in a pod, to decide which node to place the pod on. The kubelet enforces the resource limit that you specify for a container to ensure that the container is not allowed to use more than the specified limit. The kubelet also reserves the request amount of that system resource specifically for that container to use.

How to use resource metrics?

In the pod specifications, you must specify the resource requests, such as CPU and memory. The HPA uses this specification to determine the resource utilization and then scales the target up or down.

For example, the HPA object uses the following metric source:

  1. type: Resource
  2. resource:
  3.   name: cpu
  4.   target:
  5.     type: Utilization
  6.     averageUtilization: 60

In this example, the HPA keeps the average utilization of the pods in the scaling target at 60%. Utilization is the ratio between the current resource usage to the requested resource of the pod.

Best practices

All pods must have resource requests configured

The HPA makes a scaling decision based on the observed CPU or memory utilization values of pods in an OKD cluster. Utilization values are calculated as a percentage of the resource requests of each pod. Missing resource request values can affect the optimal performance of the HPA.

Configure the cool down period

During horizontal pod autoscaling, there might be a rapid scaling of events without a time gap. Configure the cool down period to prevent frequent replica fluctuations. You can specify a cool down period by configuring the stabilizationWindowSeconds field. The stabilization window is used to restrict the fluctuation of replicas count when the metrics used for scaling keep fluctuating. The autoscaling algorithm uses this window to infer a previous desired state and avoid unwanted changes to workload scale.

For example, a stabilization window is specified for the scaleDown field:

  1. behavior:
  2.   scaleDown:
  3.     stabilizationWindowSeconds: 300

In the above example, all desired states for the past 5 minutes are considered. This approximates a rolling maximum, and avoids having the scaling algorithm frequently remove pods only to trigger recreating an equivalent pod just moments later.

Scaling policies

The autoscaling/v2 API allows you to add scaling policies to a horizontal pod autoscaler. A scaling policy controls how the OKD 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

  1. apiVersion: autoscaling/v2
  2. kind: HorizontalPodAutoscaler
  3. metadata:
  4.   name: hpa-resource-metrics-memory
  5.   namespace: default
  6. spec:
  7.   behavior:
  8.     scaleDown: (1)
  9.       policies: (2)
  10.       - type: Pods (3)
  11.         value: 4 (4)
  12.         periodSeconds: 60 (5)
  13.       - type: Percent
  14.         value: 10 (6)
  15.         periodSeconds: 60
  16.       selectPolicy: Min (7)
  17.       stabilizationWindowSeconds: 300 (8)
  18.     scaleUp: (9)
  19.       policies:
  20.       - type: Pods
  21.         value: 5 (10)
  22.         periodSeconds: 70
  23.       - type: Percent
  24.         value: 12 (11)
  25.         periodSeconds: 80
  26.       selectPolicy: Max
  27.       stabilizationWindowSeconds: 0
  28. ...
1Specifies the direction for the scaling policy, either scaleDown or scaleUp. This example creates a policy for scaling down.
2Defines the scaling policy.
3Determines if the policy scales by a specific number of pods or a percentage of pods during each iteration. The default value is pods.
4Limits 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.
5Determines the length of a scaling iteration. The default value is 15 seconds.
6The default value for scaling down by percentage is 100%.
7Determines 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.
8Determines the time period the HPA should look back at desired states. The default value is 0.
9This example creates a policy for scaling up.
10Limits the amount of scaling up by the number of pods. The default value for scaling up the number of pods is 4%.
11Limits 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

  1. apiVersion: autoscaling/v2
  2. kind: HorizontalPodAutoscaler
  3. metadata:
  4.   name: hpa-resource-metrics-memory
  5.   namespace: default
  6. spec:
  7. ...
  8.   minReplicas: 20
  9. ...
  10.   behavior:
  11.     scaleDown:
  12.       stabilizationWindowSeconds: 300
  13.       policies:
  14.       - type: Pods
  15.         value: 4
  16.         periodSeconds: 30
  17.       - type: Percent
  18.         value: 10
  19.         periodSeconds: 60
  20.       selectPolicy: Max
  21.     scaleUp:
  22.       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:

  1. $ oc edit hpa hpa-resource-metrics-memory

Example output

  1. apiVersion: autoscaling/v1
  2. kind: HorizontalPodAutoscaler
  3. metadata:
  4.   annotations:
  5.     autoscaling.alpha.kubernetes.io/behavior:\
  6. '{"ScaleUp":{"StabilizationWindowSeconds":0,"SelectPolicy":"Max","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":15},{"Type":"Percent","Value":100,"PeriodSeconds":15}]},\
  7. "ScaleDown":{"StabilizationWindowSeconds":300,"SelectPolicy":"Min","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":60},{"Type":"Percent","Value":10,"PeriodSeconds":60}]}}'
  8. ...

Creating a horizontal pod autoscaler by using the web console

From the web console, you can create a horizontal pod autoscaler (HPA) that specifies the minimum and maximum number of pods you want to run on a Deployment or DeploymentConfig object. You can also define the amount of CPU or memory usage that your pods should target.

An HPA cannot be added to deployments that are part of an Operator-backed service, Knative service, or Helm chart.

Procedure

To create an HPA in the web console:

  1. In the Topology view, click the node to reveal the side pane.

  2. From the Actions drop-down list, select Add HorizontalPodAutoscaler to open the Add HorizontalPodAutoscaler form.

    Add HorizontalPodAutoscaler form

    Figure 2. Add HorizontalPodAutoscaler

  3. From the Add HorizontalPodAutoscaler form, define the name, minimum and maximum pod limits, the CPU and memory usage, and click Save.

    If any of the values for CPU and memory usage are missing, a warning is displayed.

To edit an HPA in the web console:

  1. In the Topology view, click the node to reveal the side pane.

  2. From the Actions drop-down list, select Edit HorizontalPodAutoscaler to open the Edit Horizontal Pod Autoscaler form.

  3. From the Edit Horizontal Pod Autoscaler form, edit the minimum and maximum pod limits and the CPU and memory usage, and click Save.

While creating or editing the horizontal pod autoscaler in the web console, you can switch from Form view to YAML view.

To remove an HPA in the web console:

  1. In the Topology view, click the node to reveal the side panel.

  2. From the Actions drop-down list, select Remove HorizontalPodAutoscaler.

  3. In the confirmation pop-up window, click Remove to remove the HPA.

Creating a horizontal pod autoscaler for CPU utilization by using the CLI

Using the OKD CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet object. The HPA scales the pods associated with that object to maintain the CPU usage you specify.

It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects.

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 OKD server.

To autoscale for a specific CPU value, create a HorizontalPodAutoscaler object with the target CPU and pod limits.

Prerequisites

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.

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

Example output

  1. Name:         openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
  2. Namespace:    openshift-kube-scheduler
  3. Labels:       <none>
  4. Annotations:  <none>
  5. API Version:  metrics.k8s.io/v1beta1
  6. Containers:
  7.   Name:  wait-for-host-port
  8.   Usage:
  9.     Memory:  0
  10.   Name:      scheduler
  11.   Usage:
  12.     Cpu:     8m
  13.     Memory:  45440Ki
  14. Kind:        PodMetrics
  15. Metadata:
  16.   Creation Timestamp:  2019-05-23T18:47:56Z
  17.   Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
  18. Timestamp:             2019-05-23T18:47:56Z
  19. Window:                1m0s
  20. Events:                <none>

Procedure

To create a horizontal pod autoscaler for CPU utilization:

  1. Perform one of the following:

    • To scale based on the percent of CPU utilization, create a HorizontalPodAutoscaler object for an existing object:

      1. $ oc autoscale <object_type>/<name> \(1)
      2.   --min <number> \(2)
      3.   --max <number> \(3)
      4.   --cpu-percent=<percent> (4)
      1Specify the type and name of the object to autoscale. The object must exist and be a Deployment, DeploymentConfig/dc, ReplicaSet/rs, ReplicationController/rc, or StatefulSet.
      2Optionally, specify the minimum number of replicas when scaling down.
      3Specify the maximum number of replicas when scaling up.
      4Specify 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.

      For example, the following command shows autoscaling for the image-registry Deployment object. The initial deployment requires 3 pods. The HPA object increases the minimum to 5. If CPU usage on the pods reaches 75%, the pods will increase to 7:

      1. $ oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75

    • To scale for a specific CPU value, create a YAML file similar to the following for an existing object:

      1. Create a YAML file similar to the following:

        1. apiVersion: autoscaling/v2 (1)
        2. kind: HorizontalPodAutoscaler
        3. metadata:
        4.   name: cpu-autoscale (2)
        5.   namespace: default
        6. spec:
        7.   scaleTargetRef:
        8.     apiVersion: apps/v1 (3)
        9.     kind: Deployment (4)
        10.     name: example (5)
        11.   minReplicas: 1 (6)
        12.   maxReplicas: 10 (7)
        13.   metrics: (8)
        14.   - type: Resource
        15.     resource:
        16.       name: cpu (9)
        17.       target:
        18.         type: AverageValue (10)
        19.         averageValue: 500m (11)
        1Use the autoscaling/v2 API.
        2Specify a name for this horizontal pod autoscaler object.
        3Specify the API version of the object to scale:

        • For a Deployment, ReplicaSet, Statefulset object, use apps/v1.

        • For a ReplicationController, use v1.

        • For a DeploymentConfig, use apps.openshift.io/v1.

        4Specify the type of object. The object must be a Deployment, DeploymentConfig/dc, ReplicaSet/rs, ReplicationController/rc, or StatefulSet.
        5Specify the name of the object to scale. The object must exist.
        6Specify the minimum number of replicas when scaling down.
        7Specify the maximum number of replicas when scaling up.
        8Use the metrics parameter for memory utilization.
        9Specify cpu for CPU utilization.
        10Set to AverageValue.
        11Set to averageValue with the targeted CPU value.

      2. Create the horizontal pod autoscaler:

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

  1. Verify that the horizontal pod autoscaler was created:

    1. $ oc get hpa cpu-autoscale

    Example output

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

Creating a horizontal pod autoscaler object for memory utilization by using the CLI

Using the OKD CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet object. The HPA scales the pods associated with that object to maintain the average memory utilization you specify, either a direct value or a percentage of requested memory.

It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects.

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 OKD server.

Prerequisites

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.

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

Example output

  1. Name:         openshift-kube-scheduler-ip-10-0-129-223.compute.internal
  2. Namespace:    openshift-kube-scheduler
  3. Labels:       <none>
  4. Annotations:  <none>
  5. API Version:  metrics.k8s.io/v1beta1
  6. Containers:
  7.   Name:  wait-for-host-port
  8.   Usage:
  9.     Cpu:     0
  10.     Memory:  0
  11.   Name:      scheduler
  12.   Usage:
  13.     Cpu:     8m
  14.     Memory:  45440Ki
  15. Kind:        PodMetrics
  16. Metadata:
  17.   Creation Timestamp:  2020-02-14T22:21:14Z
  18.   Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-129-223.compute.internal
  19. Timestamp:             2020-02-14T22:21:14Z
  20. Window:                5m0s
  21. Events:                <none>

Procedure

To create a horizontal pod autoscaler for memory utilization:

  1. 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 object:

      1. apiVersion: autoscaling/v2 (1)
      2. kind: HorizontalPodAutoscaler
      3. metadata:
      4.   name: hpa-resource-metrics-memory (2)
      5.   namespace: default
      6. spec:
      7.   scaleTargetRef:
      8.     apiVersion: apps/v1 (3)
      9.     kind: Deployment (4)
      10.     name: example (5)
      11.   minReplicas: 1 (6)
      12.   maxReplicas: 10 (7)
      13.   metrics: (8)
      14.   - type: Resource
      15.     resource:
      16.       name: memory (9)
      17.       target:
      18.         type: AverageValue (10)
      19.         averageValue: 500Mi (11)
      20.   behavior: (12)
      21.     scaleDown:
      22.       stabilizationWindowSeconds: 300
      23.       policies:
      24.       - type: Pods
      25.         value: 4
      26.         periodSeconds: 60
      27.       - type: Percent
      28.         value: 10
      29.         periodSeconds: 60
      30.       selectPolicy: Max
      1Use the autoscaling/v2 API.
      2Specify a name for this horizontal pod autoscaler object.
      3Specify the API version of the object to scale:

      • For a Deployment, ReplicaSet, or Statefulset object, use apps/v1.

      • For a ReplicationController, use v1.

      • For a DeploymentConfig, use apps.openshift.io/v1.

      4Specify the type of object. The object must be a Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet.
      5Specify the name of the object to scale. The object must exist.
      6Specify the minimum number of replicas when scaling down.
      7Specify the maximum number of replicas when scaling up.
      8Use the metrics parameter for memory utilization.
      9Specify memory for memory utilization.
      10Set the type to AverageValue.
      11Specify averageValue and a specific memory value.
      12Optional: 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 for an existing object:

      1. apiVersion: autoscaling/v2 (1)
      2. kind: HorizontalPodAutoscaler
      3. metadata:
      4.   name: memory-autoscale (2)
      5.   namespace: default
      6. spec:
      7.   scaleTargetRef:
      8.     apiVersion: apps/v1 (3)
      9.     kind: Deployment (4)
      10.     name: example (5)
      11.   minReplicas: 1 (6)
      12.   maxReplicas: 10 (7)
      13.   metrics: (8)
      14.   - type: Resource
      15.     resource:
      16.       name: memory (9)
      17.       target:
      18.         type: Utilization (10)
      19.         averageUtilization: 50 (11)
      20.   behavior: (12)
      21.     scaleUp:
      22.       stabilizationWindowSeconds: 180
      23.       policies:
      24.       - type: Pods
      25.         value: 6
      26.         periodSeconds: 120
      27.       - type: Percent
      28.         value: 10
      29.         periodSeconds: 120
      30.       selectPolicy: Max
      1Use the autoscaling/v2 API.
      2Specify a name for this horizontal pod autoscaler object.
      3Specify the API version of the object to scale:

      • For a ReplicationController, use v1.

      • For a DeploymentConfig, use apps.openshift.io/v1.

      • For a Deployment, ReplicaSet, Statefulset object, use apps/v1.

      4Specify the type of object. The object must be a Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet.
      5Specify the name of the object to scale. The object must exist.
      6Specify the minimum number of replicas when scaling down.
      7Specify the maximum number of replicas when scaling up.
      8Use the metrics parameter for memory utilization.
      9Specify memory for memory utilization.
      10Set to Utilization.
      11Specify 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.
      12Optional: Specify a scaling policy to control the rate of scaling up or down.

  2. Create the horizontal pod autoscaler:

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

    For example:

    1. $ oc create -f hpa.yaml

    Example output

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

  3. Verify that the horizontal pod autoscaler was created:

    1. $ oc get hpa hpa-resource-metrics-memory

    Example output

    1. NAME                          REFERENCE            TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
    2. hpa-resource-metrics-memory   Deployment/example   2441216/500Mi   1         10        1          20m
    1. $ oc describe hpa hpa-resource-metrics-memory

    Example output

    1. Name:                        hpa-resource-metrics-memory
    2. Namespace:                   default
    3. Labels:                      <none>
    4. Annotations:                 <none>
    5. CreationTimestamp:           Wed, 04 Mar 2020 16:31:37 +0530
    6. Reference:                   Deployment/example
    7. Metrics:                     ( current / target )
    8.   resource memory on pods:   2441216 / 500Mi
    9. Min replicas:                1
    10. Max replicas:                10
    11. ReplicationController pods:  1 current / 1 desired
    12. Conditions:
    13.   Type            Status  Reason              Message
    14.   ----            ------  ------              -------
    15.   AbleToScale     True    ReadyForNewScale    recommended size matches current size
    16.   ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
    17.   ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
    18. Events:
    19.   Type     Reason                   Age                 From                       Message
    20.   ----     ------                   ----                ----                       -------
    21.   Normal   SuccessfulRescale        6m34s               horizontal-pod-autoscaler  New size: 1; reason: All metrics below target

Understanding horizontal pod autoscaler status conditions by using the CLI

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 v2 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.

      1. $ oc describe hpa cm-test

      Example output

      1. Name:                           cm-test
      2. Namespace:                      prom
      3. Labels:                         <none>
      4. Annotations:                    <none>
      5. CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
      6. Reference:                      ReplicationController/cm-test
      7. Metrics:                        ( current / target )
      8.   "http_requests" on pods:      66m / 500m
      9. Min replicas:                   1
      10. Max replicas:                   4
      11. ReplicationController pods:     1 current / 1 desired
      12. Conditions: (1)
      13.   Type              Status    Reason              Message
      14.   ----              ------    ------              -------
      15.   AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
      16.   ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
      17.   ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range
      18. Events:
      1The horizontal pod autoscaler status messages.

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

Example output

  1. Conditions:
  2.   Type         Status  Reason          Message
  3.   ----         ------  ------          -------
  4.   AbleToScale  False   FailedGetScale  the HPA controller was unable to get the target's current scale: no matches for kind "ReplicationController" in group "apps"
  5. Events:
  6.   Type     Reason          Age               From                       Message
  7.   ----     ------          ----              ----                       -------
  8.   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

  1. Conditions:
  2.   Type                  Status    Reason                    Message
  3.   ----                  ------    ------                    -------
  4.   AbleToScale           True     SucceededGetScale          the HPA controller was able to get the target's current scale
  5.   ScalingActive         False    FailedGetResourceMetric    the HPA was unable to compute the replica count: failed to get cpu utilization: unable to get metrics for resource cpu: no metrics returned from resource metrics API

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

Example output

  1. Conditions:
  2.   Type              Status    Reason              Message
  3.   ----              ------    ------              -------
  4.   AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  5.   ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
  6.   ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range

Viewing horizontal pod autoscaler status conditions by using the CLI

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

The horizontal pod autoscaler status conditions are available with the v2 version of the autoscaling API.

Prerequisites

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.

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

Example output

  1. Name:         openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
  2. Namespace:    openshift-kube-scheduler
  3. Labels:       <none>
  4. Annotations:  <none>
  5. API Version:  metrics.k8s.io/v1beta1
  6. Containers:
  7.   Name:  wait-for-host-port
  8.   Usage:
  9.     Memory:  0
  10.   Name:      scheduler
  11.   Usage:
  12.     Cpu:     8m
  13.     Memory:  45440Ki
  14. Kind:        PodMetrics
  15. Metadata:
  16.   Creation Timestamp:  2019-05-23T18:47:56Z
  17.   Self Link:           /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
  18. Timestamp:             2019-05-23T18:47:56Z
  19. Window:                1m0s
  20. Events:                <none>

Procedure

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

  1. $ oc describe hpa <pod-name>

For example:

  1. $ oc describe hpa cm-test

The conditions appear in the Conditions field in the output.

Example output

  1. Name:                           cm-test
  2. Namespace:                      prom
  3. Labels:                         <none>
  4. Annotations:                    <none>
  5. CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
  6. Reference:                      ReplicationController/cm-test
  7. Metrics:                        ( current / target )
  8.   "http_requests" on pods:      66m / 500m
  9. Min replicas:                   1
  10. Max replicas:                   4
  11. ReplicationController pods:     1 current / 1 desired
  12. Conditions: (1)
  13.   Type              Status    Reason              Message
  14.   ----              ------    ------              -------
  15.   AbleToScale       True      ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  16.   ScalingActive     True      ValidMetricFound    the HPA was able to successfully calculate a replica count from pods metric http_request
  17.   ScalingLimited    False     DesiredWithinRange  the desired replica count is within the acceptable range

Additional resources