Merge pull request #50000 from jm-franc/configurable-tolerance-blog
Add HPA 'configurable tolerance' blog post (KEP-4951).pull/50488/head
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---
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layout: blog
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title: "Kubernetes v1.33: HorizontalPodAutoscaler Configurable Tolerance"
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slug: kubernetes-1-33-hpa-configurable-tolerance
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# after the v1.33 release, set a future publication date and remove the draft marker
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# the release comms team can confirm which date has been assigned
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#
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# PRs to remove the draft marker should be opened BEFORE release day
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draft: true
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math: true # for formulae
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date: XXXX-XX-XX
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author: "Jean-Marc François (Google)"
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---
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This post describes _configurable tolerance for horizontal Pod autoscaling_,
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a new alpha feature first available in Kubernetes 1.33.
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## What is it?
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[Horizontal Pod Autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/)
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is a well-known Kubernetes feature that allows your workload to
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automatically resize by adding or removing replicas based on resource
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utilization.
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Let's say you have a web application running in a Kubernetes cluster with 50
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replicas. You configure the Horizontal Pod Autoscaler (HPA) to scale based on
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CPU utilization, with a target of 75% utilization. Now, imagine that the current
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CPU utilization across all replicas is 90%, which is higher than the desired
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75%. The HPA will calculate the required number of replicas using the formula:
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```math
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desiredReplicas = ceil\left\lceil currentReplicas \times \frac{currentMetricValue}{desiredMetricValue} \right\rceil
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```
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In this example:
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```math
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50 \times (90/75) = 60
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```
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So, the HPA will increase the number of replicas from 50 to 60 to reduce the
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load on each pod. Similarly, if the CPU utilization were to drop below 75%, the
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HPA would scale down the number of replicas accordingly. The Kubernetes
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documentation provides a
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[detailed description of the scaling algorithm](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details).
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In order to avoid replicas being created or deleted whenever a small metric
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fluctuation occurs, Kubernetes applies a form of hysteresis: it only changes the
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number of replicas when the current and desired metric values differ by more
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than 10%. In the example above, since the ratio between the current and desired
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metric values is \\(90/75\\), or 20% above target, exceeding the 10% tolerance,
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the scale-up action will proceed.
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This default tolerance of 10% is cluster-wide; in older Kubernetes releases, it
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could not be fine-tuned. It's a suitable value for most usage, but too coarse
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for large deployments, where a 10% tolerance represents tens of pods. As a
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result, the community has long
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[asked](https://github.com/kubernetes/kubernetes/issues/116984) to be able to
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tune this value.
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In Kubernetes v1.33, this is now possible.
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## How do I use it?
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After enabling the `HPAConfigurableTolerance`
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[feature gate](/docs/reference/command-line-tools-reference/feature-gates/) in
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your Kubernetes v1.33 cluster, you can add your desired tolerance for your
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HorizontalPodAutoscaler object.
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Tolerances appear under the `spec.behavior.scaleDown` and
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`spec.behavior.scaleUp` fields and can thus be different for scale up and scale
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down. A typical usage would be to specify a small tolerance on scale up (to
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react quickly to spikes), but higher on scale down (to avoid adding and removing
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replicas too quickly in response to small metric fluctuations).
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For example, an HPA with a tolerance of 5% on scale-down, and no tolerance on
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scale-up, would look like the following:
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: my-app
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spec:
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...
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behavior:
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scaleDown:
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tolerance: 0.05
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scaleUp:
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tolerance: 0
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```
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## I want all the details!
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Get all the technical details by reading
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[KEP-4951](https://github.com/kubernetes/enhancements/tree/master/keps/sig-autoscaling/4951-configurable-hpa-tolerance)
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and follow [issue 4951](https://github.com/kubernetes/enhancements/issues/4951)
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to be notified of the feature graduation.
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