In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (such as
a Deployment or
StatefulSet), with the
aim of automatically scaling the workload to match demand.
Horizontal scaling means that the response to increased load is to deploy more
This is different from vertical scaling, which for Kubernetes would mean
assigning more resources (for example: memory or CPU) to the Pods that are already
running for the workload.
If the load decreases, and the number of Pods is above the configured minimum,
the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet,
or other similar resource) to scale back down.
Horizontal pod autoscaling does not apply to objects that can't be scaled (for example:
The HorizontalPodAutoscaler is implemented as a Kubernetes API resource and a
The resource determines the behavior of the controller.
The horizontal pod autoscaling controller, running within the Kubernetes
control plane, periodically adjusts the
desired scale of its target (for example, a Deployment) to match observed metrics such as average
CPU utilization, average memory utilization, or any other custom metric you specify.
There is walkthrough example of using
horizontal pod autoscaling.
HorizontalPodAutoscaler controls the scale of a Deployment and its ReplicaSet
Kubernetes implements horizontal pod autoscaling as a control loop that runs intermittently
(it is not a continuous process). The interval is set by the
--horizontal-pod-autoscaler-sync-period parameter to the
(and the default interval is 15 seconds).
Once during each period, the controller manager queries the resource utilization against the
metrics specified in each HorizontalPodAutoscaler definition. The controller manager
finds the target resource defined by the scaleTargetRef,
then selects the pods based on the target resource's .spec.selector labels, and 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
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/v2 API
version, this value can optionally be divided by the number of Pods before the
comparison is made.
The common use for HorizontalPodAutoscaler is to configure it to fetch metrics from
(metrics.k8s.io, custom.metrics.k8s.io, or external.metrics.k8s.io). The metrics.k8s.io API is
usually provided by an add-on named Metrics Server, which needs to be launched separately.
For more information about resource metrics, see
Support for metrics APIs explains the stability guarantees and support status for these
The HorizontalPodAutoscaler controller accesses corresponding workload resources that support scaling (such as Deployments
and StatefulSet). These resources each have a subresource named scale, an interface that allows you to dynamically set the
number of replicas and examine each of their current states.
For general information about subresources in the Kubernetes API, see
Kubernetes API Concepts.
From the most basic perspective, the HorizontalPodAutoscaler controller
operates on the ratio between desired metric value and current metric
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
For example, if the current metric value is 200m, and the desired value
is 100m, the number of replicas will be doubled, since 200.0 / 100.0 == 2.0 If the current value is instead 50m, you'll halve the number of
replicas, since 50.0 / 100.0 == 0.5. The control plane skips any scaling
action if the ratio is sufficiently close to 1.0 (within a globally-configurable
tolerance, 0.1 by default).
200.0 / 100.0 == 2.0
50.0 / 100.0 == 0.5
When a targetAverageValue or targetAverageUtilization is specified,
the 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, the control
plane also considers whether any metrics are missing, and how many Pods
All Pods with a deletion timestamp set (objects with a deletion timestamp are
in the process of being shut down / removed) are ignored, and all failed Pods
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 (it's still
initializing, or possibly is unhealthy) 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.
The currentMetricValue / desiredMetricValue base scale ratio is then
calculated using the remaining pods not set aside or discarded from above.
currentMetricValue / desiredMetricValue
If there were any missing metrics, the control plane recomputes 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 the workload would have
scaled up without factoring in missing metrics or not-yet-ready pods,
the controller conservatively assumes that 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, the
controller recalculates the usage ratio. If the new ratio reverses the scale
direction, or is within the tolerance, the controller doesn't take any scaling
action. In other cases, the new ratio is used to decide any change to the
number of Pods.
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
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 can be found in
the autoscaling/v2 API version which includes support for scaling on
memory and custom metrics. The new fields introduced in
autoscaling/v2 are preserved as annotations when working with
When you create a HorizontalPodAutoscaler API object, make sure the name specified is a valid
DNS subdomain name.
More details about the API object can be found at
When managing the scale of a group of replicas using the HorizontalPodAutoscaler,
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,
or flapping. It's similar to the concept of hysteresis in cybernetics.
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
manages the 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).
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 cpu and memory should
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
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.
Kubernetes v1.20 [alpha]
The HorizontalPodAutoscaler API 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:
In the above example the HPA controller scales the target such that the average utilization of the cpu
in the 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.
Kubernetes v1.23 [stable]
(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)
Provided that you use the autoscaling/v2 API version, you can configure a HorizontalPodAutoscaler
to scale based on a custom metric (that is not built in to Kubernetes or any Kubernetes component).
The HorizontalPodAutoscaler controller then queries for these custom metrics from the Kubernetes
See Support for metrics APIs for the requirements.
Provided that you use the autoscaling/v2 API version, you can specify multiple metrics for a
HorizontalPodAutoscaler to scale on. Then, the HorizontalPodAutoscaler controller evaluates each metric,
and proposes a new scale based on that metric. The HorizontalPodAutoscaler takes the maximum scale
recommended for each metric and sets the workload to that size (provided that this isn't larger than the
overall maximum that you configured).
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.io API, generally provided by metrics-server.
It can be launched as a cluster add-on.
For custom metrics, this is the custom.metrics.k8s.io API. It's provided by "adapter" API servers provided by metrics solution vendors.
Check with your metrics pipeline to see if there is a Kubernetes metrics adapter available.
For external metrics, this is the external.metrics.k8s.io API. It may be provided by the custom metrics adapters provided above.
For more information on these different metrics paths and how they differ please see the relevant design proposals for
the HPA V2,
For examples of how to use them see the walkthrough for using custom metrics
and the walkthrough for using external metrics.
If you use the v2 HorizontalPodAutoscaler API, you can use the behavior field
(see the API reference)
to configure separate scale-up and scale-down behaviors.
You specify these behaviours by setting scaleUp and / or scaleDown
under the behavior field.
You can specify a stabilization window that prevents flapping
the replica count for a scaling target. Scaling policies also let you 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:
- type: Pods
- type: Percent
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
replica 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
For example, in the following example snippet, a stabilization window is specified for scaleDown.
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 the above example, all desired states from the past 5 minutes will be 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.
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.
- type: Percent
- type: Percent
- type: Pods
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.
To provide a custom downscale stabilization window of 1 minute, the following
behavior would be added to the HPA:
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:
- type: Percent
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 selectPolicy to Min means
that the autoscaler chooses the policy that affects the smallest number of Pods:
- type: Percent
- type: Pods
The selectPolicy value of Disabled turns off scaling the given direction.
So to prevent downscaling the following policy would be used:
HorizontalPodAutoscaler, like every API resource, is supported in a standard way by kubectl.
You can create a new autoscaler using kubectl create command.
You can list autoscalers by kubectl get hpa or get detailed description by kubectl describe hpa.
Finally, you can delete an autoscaler using kubectl delete hpa.
kubectl get hpa
kubectl describe hpa
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 ReplicaSet foo, with target CPU utilization set to 80%
and the number of replicas between 2 and 5.
kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80
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
to false) until you reactivate it by manually adjusting the target's desired
replica count or HPA's minimum replica count.
When an HPA is enabled, it is recommended that the value of spec.replicas 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.
kubectl apply -f deployment.yaml
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>
When using the Server-Side Apply
you can follow the transferring ownership
guidelines, which cover this exact use case.
If you configure autoscaling in your cluster, you may also want to consider running a
cluster-level autoscaler such as Cluster Autoscaler.
For more information on HorizontalPodAutoscaler:
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