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Horizontal Pod Autoscaler
The Horizontal Pod Autoscaler automatically scales the number of Pods in a replication controller, deployment, replica set or stateful set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics). Note that Horizontal Pod Autoscaling does not apply to objects that can't be scaled, for example, DaemonSets.
The Horizontal Pod Autoscaler is implemented as a Kubernetes API resource and a controller. The resource determines the behavior of the controller. The controller periodically adjusts the number of replicas in a replication controller or deployment to match the observed metrics such as average CPU utilisation, average memory utilisation or any other custom metric to the target specified by the user.
How does the Horizontal Pod Autoscaler work?
The Horizontal Pod Autoscaler is implemented as a control loop, with a period controlled
by the controller manager's
--horizontal-pod-autoscaler-sync-period flag (with a default
value of 15 seconds).
During each period, the controller manager queries the resource utilization against the metrics specified in each HorizontalPodAutoscaler definition. The controller manager obtains the metrics from either the resource metrics API (for per-pod resource metrics), or the custom metrics API (for all other metrics).
For per-pod resource metrics (like CPU), the controller fetches the metrics from the resource metrics API for each Pod targeted by the HorizontalPodAutoscaler. Then, if a target utilization value is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each Pod. If a target raw value is set, the raw metric values are used directly. The controller then takes the mean of the utilization or the raw value (depending on the type of target specified) across all targeted Pods, and produces a ratio used to scale the number of desired replicas.
Please note that if some of the Pod's containers do not have the relevant resource request set, CPU utilization for the Pod will not be defined and the autoscaler will not take any action for that metric. See the algorithm details section below for more information about how the autoscaling algorithm works.
For per-pod custom metrics, the controller functions similarly to per-pod resource metrics, except that it works with raw values, not utilization values.
For object metrics and external metrics, a single metric is fetched, which describes the object in question. This metric is compared to the target value, to produce a ratio as above. In the
autoscaling/v2beta2API version, this value can optionally be divided by the number of Pods before the comparison is made.
The HorizontalPodAutoscaler normally fetches metrics from a series of aggregated APIs (
metrics.k8s.io API is usually provided by
metrics-server, which needs to be launched separately. For more information about resource metrics, see Metrics Server.
See Support for metrics APIs for more details.
The autoscaler accesses corresponding scalable controllers (such as replication controllers, deployments, and replica sets) by using the scale sub-resource. Scale is an interface that allows you to dynamically set the number of replicas and examine each of their current states. More details on scale sub-resource can be found here.
From the most basic perspective, the Horizontal Pod Autoscaler controller operates on the ratio between desired metric value and current metric value:
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
For example, if the current metric value is
200m, and the desired value
100m, the number of replicas will be doubled, since
200.0 / 100.0 == 2.0 If the current value is instead
50m, we'll halve the number of
50.0 / 100.0 == 0.5. We'll skip scaling if the ratio is
sufficiently close to 1.0 (within a globally-configurable tolerance, from
--horizontal-pod-autoscaler-tolerance flag, which defaults to 0.1).
targetAverageUtilization is specified,
currentMetricValue is computed by taking the average of the given
metric across all Pods in the HorizontalPodAutoscaler's scale target.
Before checking the tolerance and deciding on the final values, we take
pod readiness and missing metrics into consideration, however.
All Pods with a deletion timestamp set (i.e. Pods in the process of being shut down) and all failed Pods are discarded.
If a particular Pod is missing metrics, it is set aside for later; Pods with missing metrics will be used to adjust the final scaling amount.
When scaling on CPU, if any pod has yet to become ready (i.e. it's still initializing) or the most recent metric point for the pod was before it became ready, that pod is set aside as well.
Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when
determining whether to set aside certain CPU metrics. Instead, it
considers a Pod "not yet ready" if it's unready and transitioned to
unready within a short, configurable window of time since it started.
This value is configured with the
--horizontal-pod-autoscaler-initial-readiness-delay flag, and its default is 30
seconds. Once a pod has become ready, it considers any transition to
ready to be the first if it occurred within a longer, configurable time
since it started. This value is configured with the
--horizontal-pod-autoscaler-cpu-initialization-period flag, and its
default is 5 minutes.
currentMetricValue / desiredMetricValue base scale ratio is then
calculated using the remaining pods not set aside or discarded from above.
If there were any missing metrics, we recompute the average more conservatively, assuming those pods were consuming 100% of the desired value in case of a scale down, and 0% in case of a scale up. This dampens the magnitude of any potential scale.
Furthermore, if any not-yet-ready pods were present, and we would have scaled up without factoring in missing metrics or not-yet-ready pods, we conservatively assume the not-yet-ready pods are consuming 0% of the desired metric, further dampening the magnitude of a scale up.
After factoring in the not-yet-ready pods and missing metrics, we recalculate the usage ratio. If the new ratio reverses the scale direction, or is within the tolerance, we skip scaling. Otherwise, we use the new ratio to scale.
Note that the original value for the average utilization is reported back via the HorizontalPodAutoscaler status, without factoring in the not-yet-ready pods or missing metrics, even when the new usage ratio is used.
If multiple metrics are specified in a HorizontalPodAutoscaler, this
calculation is done for each metric, and then the largest of the desired
replica counts is chosen. If any of these metrics cannot be converted
into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs) and a scale down is suggested by the metrics which
can be fetched, scaling is skipped. This means that the HPA is still capable
of scaling up if one or more metrics give a
desiredReplicas greater than
the current value.
Finally, right before HPA scales the target, the scale recommendation is recorded. The
controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the
--horizontal-pod-autoscaler-downscale-stabilization flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values.
The Horizontal Pod Autoscaler is an API resource in the Kubernetes
autoscaling API group.
The current stable version, which only includes support for CPU autoscaling,
can be found in the
autoscaling/v1 API version.
The beta version, which includes support for scaling on memory and custom metrics,
can be found in
autoscaling/v2beta2. The new fields introduced in
are preserved as annotations when working with
Support for Horizontal Pod Autoscaler in kubectl
Horizontal Pod Autoscaler, like every API resource, is supported in a standard way by
We can create a new autoscaler using
kubectl create command.
We can list autoscalers by
kubectl get hpa and get detailed description by
kubectl describe hpa.
Finally, we can delete an autoscaler using
kubectl delete hpa.
In addition, there is a special
kubectl autoscale command for creating a HorizontalPodAutoscaler object.
For instance, executing
kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80
will create an autoscaler for replication set foo, with target CPU utilization set to
and the number of replicas between 2 and 5.
The detailed documentation of
kubectl autoscale can be found here.
Autoscaling during rolling update
Kubernetes lets you perform a rolling update on a Deployment. In that
case, the Deployment manages the underlying ReplicaSets for you.
When you configure autoscaling for a Deployment, you bind a
HorizontalPodAutoscaler to a single Deployment. The HorizontalPodAutoscaler
replicas field of the Deployment. The deployment controller is responsible
for setting the
replicas of the underlying ReplicaSets so that they add up to a suitable
number during the rollout and also afterwards.
If you perform a rolling update of a StatefulSet that has an autoscaled number of replicas, the StatefulSet directly manages its set of Pods (there is no intermediate resource similar to ReplicaSet).
Support for cooldown/delay
When managing the scale of a group of replicas using the Horizontal Pod Autoscaler, it is possible that the number of replicas keeps fluctuating frequently due to the dynamic nature of the metrics evaluated. This is sometimes referred to as thrashing.
Starting from v1.6, a cluster operator can mitigate this problem by tuning
the global HPA settings exposed as flags for the
Starting from v1.12, a new algorithmic update removes the need for the upscale delay.
--horizontal-pod-autoscaler-downscale-stabilization: Specifies the duration of the downscale stabilization time window. Horizontal Pod Autoscaler remembers the historical recommended sizes and only acts on the largest size within this time window. The default value is 5 minutes (
Support for resource metrics
Any HPA target can be scaled based on the resource usage of the pods in the scaling target.
When defining the pod specification the resource requests like
be specified. This is used to determine the resource utilization and used by the HPA controller
to scale the target up or down. To use resource utilization based scaling specify a metric source
type: Resource resource: name: cpu target: type: Utilization averageUtilization: 60
With this metric the HPA controller will keep the average utilization of the pods in the scaling target at 60%. Utilization is the ratio between the current usage of resource to the requested resources of the pod. See Algorithm for more details about how the utilization is calculated and averaged.
Container Resource Metrics
Kubernetes v1.20 [alpha]
HorizontalPodAutoscaler also supports a container metric source where the HPA can track the
resource usage of individual containers across a set of Pods, in order to scale the target resource.
This lets you configure scaling thresholds for the containers that matter most in a particular Pod.
For example, if you have a web application and a logging sidecar, you can scale based on the resource
use of the web application, ignoring the sidecar container and its resource use.
If you revise the target resource to have a new Pod specification with a different set of containers, you should revise the HPA spec if that newly added container should also be used for scaling. If the specified container in the metric source is not present or only present in a subset of the pods then those pods are ignored and the recommendation is recalculated. See Algorithm for more details about the calculation. To use container resources for autoscaling define a metric source as follows:
type: ContainerResource containerResource: name: cpu container: application target: type: Utilization averageUtilization: 60
In the above example the HPA controller scales the target such that the average utilization of the cpu
application container of all the pods is 60%.
If you change the name of a container that a HorizontalPodAutoscaler is tracking, you can make that change in a specific order to ensure scaling remains available and effective whilst the change is being applied. Before you update the resource that defines the container (such as a Deployment), you should update the associated HPA to track both the new and old container names. This way, the HPA is able to calculate a scaling recommendation throughout the update process.
Once you have rolled out the container name change to the workload resource, tidy up by removing the old container name from the HPA specification.
Support for multiple metrics
Kubernetes 1.6 adds support for scaling based on multiple metrics. You can use the
version to specify multiple metrics for the Horizontal Pod Autoscaler to scale on. Then, the Horizontal Pod
Autoscaler controller will evaluate each metric, and propose a new scale based on that metric. The largest of the
proposed scales will be used as the new scale.
Support for custom metrics
Kubernetes 1.6 adds support for making use of custom metrics in the Horizontal Pod Autoscaler.
You can add custom metrics for the Horizontal Pod Autoscaler to use in the
Kubernetes then queries the new custom metrics API to fetch the values of the appropriate custom metrics.
See Support for metrics APIs for the requirements.
Support for metrics APIs
By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs. In order for it to access these APIs, cluster administrators must ensure that:
The API aggregation layer is enabled.
The corresponding APIs are registered:
For resource metrics, this is the
metrics.k8s.ioAPI, generally provided by metrics-server. It can be launched as a cluster addon.
For custom metrics, this is the
custom.metrics.k8s.ioAPI. It's provided by "adapter" API servers provided by metrics solution vendors. Check with your metrics pipeline, or the list of known solutions. If you would like to write your own, check out the boilerplate to get started.
For external metrics, this is the
external.metrics.k8s.ioAPI. It may be provided by the custom metrics adapters provided above.
Support for configurable scaling behavior
v2beta2 API allows scaling behavior to be configured through the HPA
behavior field. Behaviors are specified separately for scaling up and down in
scaleDown section under the
behavior field. A stabilization
window can be specified for both directions which prevents the flapping of the
number of the replicas in the scaling target. Similarly specifying scaling
policies controls the rate of change of replicas while scaling.
One or more scaling policies can be specified in the
behavior section of the spec.
When multiple policies are specified the policy which allows the highest amount of
change is the policy which is selected by default. The following example shows this behavior
while scaling down:
behavior: scaleDown: policies: - type: Pods value: 4 periodSeconds: 60 - type: Percent value: 10 periodSeconds: 60
periodSeconds indicates the length of time in the past for which the policy must hold true.
The first policy (Pods) allows at most 4 replicas to be scaled down in one minute. The second policy
(Percent) allows at most 10% of the current replicas to be scaled down in one minute.
Since by default the policy which allows the highest amount of change is selected, the second policy will only be used when the number of pod replicas is more than 40. With 40 or less replicas, the first policy will be applied. For instance if there are 80 replicas and the target has to be scaled down to 10 replicas then during the first step 8 replicas will be reduced. In the next iteration when the number of replicas is 72, 10% of the pods is 7.2 but the number is rounded up to 8. On each loop of the autoscaler controller the number of pods to be change is re-calculated based on the number of current replicas. When the number of replicas falls below 40 the first policy (Pods) is applied and 4 replicas will be reduced at a time.
The policy selection can be changed by specifying the
selectPolicy field for a scaling
direction. By setting the value to
Min which would select the policy which allows the
smallest change in the replica count. Setting the value to
Disabled completely disables
scaling in that direction.
The stabilization window is used to restrict the flapping of replicas when the metrics
used for scaling keep fluctuating. The stabilization window is used by the autoscaling
algorithm to consider the computed desired state from the past to prevent scaling. In
the following example the stabilization window is specified for
scaleDown: stabilizationWindowSeconds: 300
When the metrics indicate that the target should be scaled down the algorithm looks into previously computed desired states and uses the highest value from the specified interval. In above example all desired states from the past 5 minutes will be considered.
To use the custom scaling not all fields have to be specified. Only values which need to be customized can be specified. These custom values are merged with default values. The default values match the existing behavior in the HPA algorithm.
behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Percent value: 100 periodSeconds: 15 scaleUp: stabilizationWindowSeconds: 0 policies: - type: Percent value: 100 periodSeconds: 15 - type: Pods value: 4 periodSeconds: 15 selectPolicy: Max
For scaling down the stabilization window is 300 seconds (or the value of the
--horizontal-pod-autoscaler-downscale-stabilization flag if provided). There is only a single policy
for scaling down which allows a 100% of the currently running replicas to be removed which
means the scaling target can be scaled down to the minimum allowed replicas.
For scaling up there is no stabilization window. When the metrics indicate that the target should be
scaled up the target is scaled up immediately. There are 2 policies where 4 pods or a 100% of the currently
running replicas will be added every 15 seconds till the HPA reaches its steady state.
Example: change downscale stabilization window
To provide a custom downscale stabilization window of 1 minute, the following behavior would be added to the HPA:
behavior: scaleDown: stabilizationWindowSeconds: 60
Example: limit scale down rate
To limit the rate at which pods are removed by the HPA to 10% per minute, the following behavior would be added to the HPA:
behavior: scaleDown: policies: - type: Percent value: 10 periodSeconds: 60
To ensure that no more than 5 Pods are removed per minute, you can add a second scale-down
policy with a fixed size of 5, and set
selectPolicy to minimum. Setting
that the autoscaler chooses the policy that affects the smallest number of Pods:
behavior: scaleDown: policies: - type: Percent value: 10 periodSeconds: 60 - type: Pods value: 5 periodSeconds: 60 selectPolicy: Min
Example: disable scale down
selectPolicy value of
Disabled turns off scaling the given direction.
So to prevent downscaling the following policy would be used:
behavior: scaleDown: selectPolicy: Disabled
Implicit maintenance-mode deactivation
You can implicitly deactivate the HPA for a target without the
need to change the HPA configuration itself. If the target's desired replica count
is set to 0, and the HPA's minimum replica count is greater than 0, the HPA
stops adjusting the target (and sets the
ScalingActive Condition on itself
false) until you reactivate it by manually adjusting the target's desired
replica count or HPA's minimum replica count.
Migrating Deployments and StatefulSets to horizontal autoscaling
When an HPA is enabled, it is recommended that the value of
the Deployment and / or StatefulSet be removed from their
manifest(s). If this isn't done, any time
a change to that object is applied, for example via
kubectl apply -f deployment.yaml, this will instruct Kubernetes to scale the current number of Pods
to the value of the
spec.replicas key. This may not be
desired and could be troublesome when an HPA is active.
Keep in mind that the removal of
spec.replicas may incur a one-time
degradation of Pod counts as the default value of this key is 1 (reference
Upon the update, all Pods except 1 will begin their termination procedures. Any
deployment application afterwards will behave as normal and respect a rolling
update configuration as desired. You can avoid this degradation by choosing one of the following two
methods based on how you are modifying your deployments:
kubectl apply edit-last-applied deployment/<deployment_name>
- In the editor, remove
spec.replicas. When you save and exit the editor,
kubectlapplies the update. No changes to Pod counts happen at this step.
- You can now remove
spec.replicasfrom the manifest. If you use source code management, also commit your changes or take whatever other steps for revising the source code are appropriate for how you track updates.
- From here on out you can run
kubectl apply -f deployment.yaml