怎样使用自定义指标进行K8S自动弹性伸缩

Kubernetes provides users with a set of APIs that can be used to manage the deployment and scaling of applications. One of these APIs is the Custom Metrics API, which allows users to define their own custom metrics to be used for autoscaling purposes.

In order to use the Custom Metrics API, you first need to create a custom metric definition file. This file defines the metric name, units, and type (gauge, counter, etc.). An example metric definition file is shown below:

apiVersion: autoscaling.k8s.io/v1 kind: MetricDefinition metadata: name: my-custom-metric namespace: default spec: type: Counter units: requests/second

Once you have created your metric definition file, you can then create a custom metric object. This object contains the actual metric data that you want to use for autoscaling. An example custom metric object is shown below:

apiVersion: autoscaling.k8s.io/v1 kind: CustomMetric metadata: name: my-custom-metric namespace: default spec: metricName: my-custom-metric metricLabels: app: my-app stage: prod value: 3

Once you have created your custom metric object, you can then use it in your autoscaling policy. An example autoscaling policy is shown below:

apiVersion: autoscaling.k8s.io/v1 kind: HorizontalPodAutoscaler metadata: name: my-hpa namespace: default spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-deployment minReplicas: 3 maxReplicas: 10 metrics: - type: CustomMetric customMetric: name: my-custom-metric targetAverageValue: 10

In the above example, the autoscaling policy will scale the my-deployment deployment up or down based on the value of the my-custom-metric metric. If the average value of the metric is below 10, the deployment will be scaled up. If the average value of the metric is above 10, the deployment will be scaled down.

You can also use the Custom Metrics API to create autoscaling policies that scale based on multiple metrics. An example policy that scales based on two metrics is shown below:

apiVersion: autoscaling.k8s.io/v1 kind: HorizontalPodAutoscaler metadata: name: my-hpa namespace: default spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-deployment minReplicas: 3 maxReplicas: 10 metrics: - type: CustomMetric customMetric: name: my-custom-metric-1 targetAverageValue: 10 - type: CustomMetric customMetric: name: my-custom-metric-2 targetAverageValue: 20

In the above example, the autoscaling policy will scale the my-deployment deployment up or down based on the average value of the my-custom-metric-1 and my-custom-metric-2 metrics. If the average value of both metrics is below 10, the deployment will be scaled up. If the average value of both metrics is above 10, the deployment will be scaled down.

The Custom Metrics API is a powerful tool that can be used to create autoscaling policies that are tailored to your specific needs. By creating your own custom metrics, you can ensure that your applications are scaled in the way that you want them to be.