Written horizontal-pod-autoscaling-en. (#1153)
* Written horizontal-pod-autoscaling-en. Signed-off-by: PatrickLuoYu <PatrickLuoYu@PatrickLuoYu.local> * Renamed file. Signed-off-by: Patrick-LuoYu <patrickluo@yunify.com> * Fixed issues based on comments. Signed-off-by: Patrick-LuoYu <patrickluo@yunify.com> * Update horizontal-pod-autoscaling.md Co-authored-by: PatrickLuoYu <PatrickLuoYu@PatrickLuoYu.local> Co-authored-by: Sherlock113 <65327072+Sherlock113@users.noreply.github.com>
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title: "Horizontal Pod Autoscaling"
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keywords: "Horizontal, Pod, Autoscaling, Autoscaler"
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description: "How to configure Horizontal Pod Autoscaling on KubeSphere."
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weight: 10290
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---
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This document describes how to configure Horizontal Pod Autoscaling (HPA) on KubeSphere.
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The HPA feature automatically adjusts the number of Pods to maintain average resource usage (CPU and memory) of Pods around preset values. For details about how HPA functions, see the [official Kubernetes document](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/).
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This document uses HPA based on CPU usage as an example. Operations for HPA based on memory usage are similar.
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## Prerequisites
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- You need to [enable the Metrics Server](https://kubesphere.io/docs/pluggable-components/metrics-server/).
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- You need to create a workspace, a project and an account (for example, `project-regular`). `project-regular` must be invited to the project and assigned the `operator` role. For more information, see [Create Workspaces, Projects, Accounts and Roles](/docs/quick-start/create-workspace-and-project/).
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## Create a Service
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1. Log in to the KubeSphere web console as `project-regular` and go to your project.
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2. Choose **Services** in **Application Workloads** on the left navigation bar and click **Create** on the right.
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3. In the **Create Service** dialog box, click **Stateless Service**.
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4. Set the Service name (for example, `hpa`) and click **Next**.
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5. Click **Add Container Image**, set **Image** to `mirrorgooglecontainers/hpa-example` and click **Use Default Ports**.
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6. Set the CPU request (for example, 0.15 cores) for each container, click **√**, and click **Next**.
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{{< notice note >}}
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* To use HPA based on CPU usage, you must set the CPU request for each container, which is the minimum CPU resource reserved for each container (for details, see the [official Kubernetes document](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/)). The HPA feature compares the average Pod CPU usage with a target percentage of the average Pod CPU request.
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* For HPA based on memory usage, you do not need to configure the memory request.
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{{</ notice >}}
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7. Click **Next** on the **Mount Volumes** tab and click **Create** on the **Advanced Settings** tab.
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## Configure HPA
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1. Choose **Deployments** in **Workloads** on the left navigation bar and click the HPA Deployment (for example, hpa-v1) on the right.
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2. Click **More** and choose **Horizontal Pod Autoscaling** from the drop-down list.
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3. In the **Horizontal Pod Autoscaling** dialog box, configure the HPA parameters and click **OK**.
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* **CPU Target Utilization**: Target percentage of the average Pod CPU request.
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* **Memory Target Usage**: Target average Pod memory usage in MiB.
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* **Min Replicas Number**: Minimum number of Pods.
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* **Max Replicas Number**: Maximum number of Pods.
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In this example, **CPU Target Utilization** is set to `60`, **Min Replicas Number** is set to `1`, and **Max Replicas Number** is set to `10`.
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{{< notice note >}}
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Ensure that the cluster can provide sufficient resource for all Pods when the number of Pods reaches the maximum. Otherwise, the creation of some Pods will fail.
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{{</ notice >}}
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## Verify HPA
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This section uses a Deployment that sends requests to the HPA Service to verify that HPA automatically adjusts the number of Pods to meet the resource usage target.
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### Create a load generator Deployment
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1. Choose **Workloads** in **Application Workloads** on the left navigation bar and click **Create** on the right.
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2. In the **Create Deployment** dialog box, set the Deployment name (for example, `load-generator`) and click **Next**.
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3. Click **Add Container Image** and set **Image** to `busybox`.
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4. Scroll down in the dialog box, select **Start Command**, and set **Run Command** to `sh,-c` and **Parameters** to `while true; do wget -q -O- http://<Target Service>.<Target project>.svc.cluster.local; done` (for example, `while true; do wget -q -O- http://hpa.demo-project.svc.cluster.local; done`).
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5. Click **√** and click **Next**.
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6. Click **Next** on the **Mount Volumes** tab and click **Create** on the **Advanced Settings** tab.
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### View the HPA Deployment status
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1. After the load generator Deployment is created, choose **Workloads** in **Application Workloads** on the left navigation bar and click the HPA Deployment (for example, hpa-v1) on the right.
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The number of Pods automatically increases to meet the resource usage target.
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2. Choose **Workloads** in **Application Workloads** on the left navigation bar, click the three dots on the right of the load generator Deployment (for example, load-generator-v1), and choose **Delete** from the drop-down list. After the load-generator Deployment is deleted, check the status of the HPA Deployment again.
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The number of Pods decreases to the minimum.
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{{< notice note >}}
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The system may require a few minutes to adjust the number of Pods and collect data.
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{{</ notice >}}
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## Edit HPA Configuration
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You can repeat steps in [Configure HPA](#configure-hpa) to edit the HPA configuration.
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## Cancel HPA
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1. Choose **Workloads** in **Application Workloads** on the left navigation bar and click the HPA Deployment (for example, hpa-v1) on the right.
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2. Click the three dots on the right of **Horizontal Pod Autoscaling** and choose **Cancel** from the drop-down list.
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