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Improve docs for HorizontalPodAutoscaler

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Co-authored-by: Chris Negus <cnegus@redhat.com>
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Tim Bannister
2021-11-23 17:18:57 +00:00
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content/en/docs/tasks/run-application/horizontal-pod-autoscale.md
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@@ -3,41 +3,59 @@ reviewers:
- fgrzadkowski
- jszczepkowski
- directxman12
title: Horizontal Pod Autoscaler
title: Horizontal Pod Autoscaling
feature:
title: Horizontal scaling
description: >
Scale your application up and down with a simple command, with a UI, or automatically based on CPU usage.
content_type: concept
weight: 90
---
<!-- overview -->
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](https://git.k8s.io/community/contributors/design-proposals/instrumentation/custom-metrics-api.md)
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.
In Kubernetes, a _HorizontalPodAutoscaler_ automatically updates a workload resource (such as
a {{< glossary_tooltip text="Deployment" term_id="deployment" >}} or
{{< glossary_tooltip text="StatefulSet" term_id="statefulset" >}}), with the
aim of automatically scaling the workload to match demand.
The Horizontal Pod Autoscaler is implemented as a Kubernetes API resource and a controller.
Horizontal scaling means that the response to increased load is to deploy more
{{< glossary_tooltip text="Pods" term_id="pod" >}}.
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:
a {{< glossary_tooltip text="DaemonSet" term_id="daemonset" >}}.)
The HorizontalPodAutoscaler is implemented as a Kubernetes API resource and a
{{< glossary_tooltip text="controller" term_id="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.
The horizontal pod autoscaling controller, running within the Kubernetes
{{< glossary_tooltip text="control plane" term_id="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](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/) of using
horizontal pod autoscaling.
<!-- body -->
## How does the Horizontal Pod Autoscaler work?
## How does a HorizontalPodAutoscaler work?
![Horizontal Pod Autoscaler diagram](/images/docs/horizontal-pod-autoscaler.svg)
{{< figure src="/images/docs/horizontal-pod-autoscaler.svg" caption="HorizontalPodAutoscaler controls the scale of a Deployment and its ReplicaSet" class="diagram-medium">}}
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).
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
[`kube-controller-manager`](/docs/reference/command-line-tools-reference/kube-controller-manager/)
(and the default interval is 15 seconds).
During each period, the controller manager queries the resource utilization against the
Once 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).
@@ -45,17 +63,17 @@ 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.
value as a percentage of the equivalent
[resource request](/docs/concepts/configuration/manage-resources-containers/#requests-and-limits)
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](#algorithm-details) section below for more information about
how the autoscaling algorithm works.
not take any action for that metric. See the [algorithm details](#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.
@@ -66,20 +84,25 @@ or the custom metrics API (for all other metrics).
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`,
`custom.metrics.k8s.io`, and `external.metrics.k8s.io`). The `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](/docs/tasks/debug-application-cluster/resource-metrics-pipeline/#metrics-server).
The common use for HorizontalPodAutoscaler is to configure it to fetch metrics from
{{< glossary_tooltip text="aggregated APIs" term_id="aggregation-layer" >}}
(`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
[Metrics Server](/docs/tasks/debug-application-cluster/resource-metrics-pipeline/#metrics-server).
See [Support for metrics APIs](#support-for-metrics-apis) for more details.
[Support for metrics APIs](#support-for-metrics-apis) explains the stability guarantees and support status for these
different APIs.
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](https://git.k8s.io/community/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md#scale-subresource).
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](/docs/reference/using-api/api-concepts/).
### Algorithm Details
### Algorithm details
From the most basic perspective, the Horizontal Pod Autoscaler controller
From the most basic perspective, the HorizontalPodAutoscaler controller
operates on the ratio between desired metric value and current metric
value:
@@ -89,26 +112,28 @@ desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricVal
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`, we'll halve the number of
replicas, since `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
the `--horizontal-pod-autoscaler-tolerance` flag, which defaults to 0.1).
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).
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, 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.
Before checking the tolerance and deciding on the final values, the control
plane also considers whether any metrics are missing, and how many Pods
are [`Ready`](/docs/concepts/workloads/pods/pod-lifecycle/#pod-conditions).
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
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.
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
@@ -124,20 +149,21 @@ default is 5 minutes.
The `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
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 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.
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, 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.
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
@@ -173,19 +199,13 @@ When you create a HorizontalPodAutoscaler API object, make sure the name specifi
More details about the API object can be found at
[HorizontalPodAutoscaler Object](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#horizontalpodautoscaler-v2-autoscaling).
## Stability of workload scale {#flapping}
## Support for Horizontal Pod Autoscaler in kubectl
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.
Horizontal Pod Autoscaler, like every API resource, is supported in a standard way by `kubectl`.
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 `80%`
and the number of replicas between 2 and 5.
The detailed documentation of `kubectl autoscale` can be found [here](/docs/reference/generated/kubectl/kubectl-commands/#autoscale).
## Autoscaling during rolling update
@@ -202,31 +222,6 @@ If you perform a rolling update of a StatefulSet that has an autoscaled number o
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 `kube-controller-manager` component:
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 (`5m0s`).
{{< note >}}
When tuning these parameter values, a cluster operator should be aware of the possible
consequences. If the delay (cooldown) value is set too long, there could be complaints
that the Horizontal Pod Autoscaler is not responsive to workload changes. However, if
the delay value is set too short, the scale of the replicas set may keep thrashing as
usual.
{{< /note >}}
## Support for resource metrics
Any HPA target can be scaled based on the resource usage of the pods in the scaling target.
@@ -255,11 +250,11 @@ a single container might be running with high usage and the HPA will not scale o
pod usage is still within acceptable limits.
{{< /note >}}
### Container Resource Metrics
### Container resource metrics
{{< feature-state for_k8s_version="v1.20" state="alpha" >}}
`HorizontalPodAutoscaler` also supports a container metric source where the HPA can track the
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
@@ -271,6 +266,7 @@ scaling. If the specified container in the metric source is not present or only
of the pods then those pods are ignored and the recommendation is recalculated. See [Algorithm](#algorithm-details)
for more details about the calculation. To use container resources for autoscaling define a metric
source as follows:
```yaml
type: ContainerResource
containerResource:
@@ -296,30 +292,32 @@ Once you have rolled out the container name change to the workload resource, tid
the old container name from the HPA specification.
{{< /note >}}
## Support for multiple metrics
Kubernetes 1.6 adds support for scaling based on multiple metrics. You can use the `autoscaling/v2` API
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.
## Scaling on custom metrics
## Support for custom metrics
{{< feature-state for_k8s_version="v1.23" state="stable" >}}
{{< note >}}
Kubernetes 1.2 added alpha support for scaling based on application-specific metrics using special annotations.
Support for these annotations was removed in Kubernetes 1.6 in favor of the new autoscaling API. While the old method for collecting
custom metrics is still available, these metrics will not be available for use by the Horizontal Pod Autoscaler, and the former
annotations for specifying which custom metrics to scale on are no longer honored by the Horizontal Pod Autoscaler controller.
{{< /note >}}
(the `autoscaling/v2beta2` API version previously provided this ability as a beta feature)
You can also use a HorizontalPodAutoscaler to change the scale of a
workload based on custom metrics. You can add custom metrics for the
Horizontal Pod Autoscaler to use in the `autoscaling/v2` API.
Kubernetes then queries the new custom metrics API to fetch the values
of the appropriate custom metrics.
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
API.
See [Support for metrics APIs](#support-for-metrics-apis) for the requirements.
## Scaling on multiple metrics
{{< feature-state for_k8s_version="v1.23" state="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 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).
## Support for metrics APIs
By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs. In order for it to access these
@@ -333,8 +331,7 @@ APIs, cluster administrators must ensure that:
It can be launched as a cluster addon.
* 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, or the [list of known solutions](https://github.com/kubernetes/metrics/blob/master/IMPLEMENTATIONS.md#custom-metrics-api).
If you would like to write your own, check out the [boilerplate](https://github.com/kubernetes-sigs/custom-metrics-apiserver) to get started.
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.
@@ -346,20 +343,23 @@ and [external.metrics.k8s.io](https://github.com/kubernetes/community/blob/maste
For examples of how to use them see [the walkthrough for using custom metrics](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics)
and [the walkthrough for using external metrics](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-metrics-not-related-to-kubernetes-objects).
## Support for configurable scaling behavior
## Configurable scaling behavior
Starting from
[v1.18](https://github.com/kubernetes/enhancements/blob/master/keps/sig-autoscaling/853-configurable-hpa-scale-velocity/README.md)
the `v2beta2` API (and from v1.23 the `v2` API) allows scaling
behavior to be configured through the HPA `behavior` field. Behaviors
are specified separately for scaling up and down in `scaleUp` or
`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.
{{< feature-state for_k8s_version="v1.23" state="stable" >}}
### Scaling Policies
(the `autoscaling/v2beta2` API version previously provided this ability as a beta feature)
If you use the `v2` HorizontalPodAutoscaler API, you can use the `behavior` field
(see the [API reference](/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler-v2/#HorizontalPodAutoscalerSpec))
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](#flapping)
the replica count for a scaling target. Scaling policies also let you controls the
rate of change of replicas while scaling.
### Scaling policies
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
@@ -396,21 +396,27 @@ direction. By setting the value to `Min` which would select the policy which all
smallest change in the replica count. Setting the value to `Disabled` completely disables
scaling in that direction.
### Stabilization Window
### Stabilization window
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`.
The stabilization window is used to restrict the [flapping](#flapping) 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, in the following example snippet, a stabilization window is specified for `scaleDown`.
```yaml
scaleDown:
stabilizationWindowSeconds: 300
behavior:
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.
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.
### Default Behavior
@@ -498,6 +504,18 @@ behavior:
selectPolicy: Disabled
```
## Support for HorizontalPodAutoscaler in kubectl
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`.
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 `80%`
and the number of replicas between 2 and 5.
## Implicit maintenance-mode deactivation
You can implicitly deactivate the HPA for a target without the
@@ -509,7 +527,13 @@ replica count or HPA's minimum replica count.
## {{% heading "whatsnext" %}}
If you configure autoscaling in your cluster, you may also want to consider running a
cluster-level autoscaler such as [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler).
* Design documentation: [Horizontal Pod Autoscaling](https://git.k8s.io/community/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md).
* kubectl autoscale command: [kubectl autoscale](/docs/reference/generated/kubectl/kubectl-commands/#autoscale).
* Usage example of [Horizontal Pod Autoscaler](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/).
For more information on HorizontalPodAutoscaler:
* Read a [walkthrough example](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/) for horizontal pod autoscaling.
* Read documentation for [`kubectl autoscale`](/docs/reference/generated/kubectl/kubectl-commands/#autoscale).
* If you would like to write your own custom metrics adapter, check out the
[boilerplate](https://github.com/kubernetes-sigs/custom-metrics-apiserver) to get started.
* Read the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler/) for HorizontalPodAutoscaler.