k8s与HPA--通过 Prometheus adaptor 来自定义监控指标

栏目: 数据库 · 发布时间: 5年前

内容简介：自动扩展是一种根据资源使用情况自动扩展或缩小工作负载的方法。 Kubernetes中的自动缩放有两个维度：Cluster Autoscaler处理节点扩展操作，Horizo​​ntal Pod Autoscaler自动扩展部署或副本集中的pod数量。 Cluster Autoscaling与Horizo​​ntal Pod Autoscaler一起用于动态调整计算能力以及系统满足SLA所需的并行度。虽然Cluster Autoscaler高度依赖托管您的集群的云提供商的基础功能，但HPA可以独立于您的Iaa

k8s与HPA--通过 Prometheus adaptor 来自定义监控指标

自动扩展是一种根据资源使用情况自动扩展或缩小工作负载的方法。 Kubernetes中的自动缩放有两个维度：Cluster Autoscaler处理节点扩展操作，Horizo​​ntal Pod Autoscaler自动扩展部署或副本集中的pod数量。 Cluster Autoscaling与Horizo​​ntal Pod Autoscaler一起用于动态调整计算能力以及系统满足SLA所需的并行度。虽然Cluster Autoscaler高度依赖托管您的集群的云提供商的基础功能，但HPA可以独立于您的IaaS / PaaS提供商运营。

Horizo​​ntal Pod Autoscaler功能最初是在Kubernetes v1.1中引入的，并且从那时起已经发展了很多。 HPA缩放容器的版本1基于观察到的CPU利用率，后来基于内存使用情况。在Kubernetes 1.6中，引入了一个新的API Custom Metrics API，使HPA能够访问任意指标。 Kubernetes 1.7引入了聚合层，允许第三方应用程序通过将自己注册为API附加组件来扩展Kubernetes API。 Custom Metrics API和聚合层使Prometheus等监控系统可以向HPA控制器公开特定于应用程序的指标。

Horizo​​ntal Pod Autoscaler实现为一个控制循环，定期查询Resource Metrics API以获取CPU /内存等核心指标和针对特定应用程序指标的Custom Metrics API。

k8s与HPA--通过 Prometheus adaptor 来自定义监控指标

以下是为Kubernetes 1.9或更高版本配置HPA v2的分步指南。您将安装提供核心指标的Metrics Server附加组件，然后您将使用演示应用程序根据CPU和内存使用情况展示pod自动扩展。在本指南的第二部分中，您将部署Prometheus和自定义API服务器。您将使用聚合器层注册自定义API服务器，然后使用演示应用程序提供的自定义指标配置HPA。

在开始之前，您需要安装 Go 1.8或更高版本并在GOPATH中克隆k8s-prom-hpa repo。

cd $GOPATH
git clone https://github.com/stefanprodan/k8s-prom-hpa

部署 Metrics Server

kubernetes Metrics Server是资源使用数据的集群范围聚合器，是Heapster的后继者。度量服务器通过汇集来自kubernetes.summary_api的数据来收集节点和pod的CPU和内存使用情况。摘要API是一种内存高效的API，用于将数据从Kubelet / cAdvisor传递到度量服务器。

k8s与HPA--通过 Prometheus adaptor 来自定义监控指标

在HPA的第一个版本中，您需要Heapster来提供CPU和内存指标，在HPA v2和Kubernetes 1.8中，只有在启用horizo​​ntal-pod-autoscaler-use-rest-clients时才需要指标服务器。默认情况下，Kubernetes 1.9中启用了HPA rest客户端。 GKE 1.9附带预安装的Metrics Server。

在kube-system命名空间中部署Metrics Server：

kubectl create -f ./metrics-server

一分钟后，度量服务器开始报告节点和pod的CPU和内存使用情况。

查看nodes metrics：

kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes" | jq .

结果如下：

{
  "kind": "NodeMetricsList",
  "apiVersion": "metrics.k8s.io/v1beta1",
  "metadata": {
    "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes"
  },
  "items": [
    {
      "metadata": {
        "name": "ip-10-1-50-61.ec2.internal",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/ip-10-1-50-61.ec2.internal",
        "creationTimestamp": "2019-02-13T08:34:05Z"
      },
      "timestamp": "2019-02-13T08:33:38Z",
      "window": "30s",
      "usage": {
        "cpu": "78322168n",
        "memory": "563180Ki"
      }
    },
    {
      "metadata": {
        "name": "ip-10-1-57-40.ec2.internal",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/ip-10-1-57-40.ec2.internal",
        "creationTimestamp": "2019-02-13T08:34:05Z"
      },
      "timestamp": "2019-02-13T08:33:42Z",
      "window": "30s",
      "usage": {
        "cpu": "48926263n",
        "memory": "554472Ki"
      }
    },
    {
      "metadata": {
        "name": "ip-10-1-62-29.ec2.internal",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/ip-10-1-62-29.ec2.internal",
        "creationTimestamp": "2019-02-13T08:34:05Z"
      },
      "timestamp": "2019-02-13T08:33:36Z",
      "window": "30s",
      "usage": {
        "cpu": "36700681n",
        "memory": "326088Ki"
      }
    }
  ]
}

查看pods metrics:

kubectl get --raw "/apis/metrics.k8s.io/v1beta1/pods" | jq .

结果如下：

{
  "kind": "PodMetricsList",
  "apiVersion": "metrics.k8s.io/v1beta1",
  "metadata": {
    "selfLink": "/apis/metrics.k8s.io/v1beta1/pods"
  },
  "items": [
    {
      "metadata": {
        "name": "kube-proxy-77nt2",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-proxy-77nt2",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:00Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube-proxy",
          "usage": {
            "cpu": "2370555n",
            "memory": "13184Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "cluster-autoscaler-n2xsl",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/cluster-autoscaler-n2xsl",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:12Z",
      "window": "30s",
      "containers": [
        {
          "name": "cluster-autoscaler",
          "usage": {
            "cpu": "1477997n",
            "memory": "54584Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "core-dns-autoscaler-b4785d4d7-j64xd",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/core-dns-autoscaler-b4785d4d7-j64xd",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:08Z",
      "window": "30s",
      "containers": [
        {
          "name": "autoscaler",
          "usage": {
            "cpu": "191293n",
            "memory": "7956Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "spot-interrupt-handler-8t2xk",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/spot-interrupt-handler-8t2xk",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:04Z",
      "window": "30s",
      "containers": [
        {
          "name": "spot-interrupt-handler",
          "usage": {
            "cpu": "844907n",
            "memory": "4608Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "kube-proxy-t5kqm",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-proxy-t5kqm",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:08Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube-proxy",
          "usage": {
            "cpu": "1194766n",
            "memory": "12204Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "kube-proxy-zxmqb",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-proxy-zxmqb",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:06Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube-proxy",
          "usage": {
            "cpu": "3021117n",
            "memory": "13628Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "aws-node-rcz5c",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/aws-node-rcz5c",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:15Z",
      "window": "30s",
      "containers": [
        {
          "name": "aws-node",
          "usage": {
            "cpu": "1217989n",
            "memory": "24976Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "aws-node-z2qxs",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/aws-node-z2qxs",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:15Z",
      "window": "30s",
      "containers": [
        {
          "name": "aws-node",
          "usage": {
            "cpu": "1025780n",
            "memory": "46424Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "php-apache-899d75b96-8ppk4",
        "namespace": "default",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/default/pods/php-apache-899d75b96-8ppk4",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:08Z",
      "window": "30s",
      "containers": [
        {
          "name": "php-apache",
          "usage": {
            "cpu": "24612n",
            "memory": "27556Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "load-generator-779c5f458c-9sglg",
        "namespace": "default",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/default/pods/load-generator-779c5f458c-9sglg",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:34:56Z",
      "window": "30s",
      "containers": [
        {
          "name": "load-generator",
          "usage": {
            "cpu": "0",
            "memory": "336Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "aws-node-v9jxs",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/aws-node-v9jxs",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:00Z",
      "window": "30s",
      "containers": [
        {
          "name": "aws-node",
          "usage": {
            "cpu": "1303458n",
            "memory": "28020Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "kube2iam-m2ktt",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube2iam-m2ktt",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:11Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube2iam",
          "usage": {
            "cpu": "1328864n",
            "memory": "9724Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "kube2iam-w9cqf",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube2iam-w9cqf",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:03Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube2iam",
          "usage": {
            "cpu": "1294379n",
            "memory": "8812Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "custom-metrics-apiserver-657644489c-pk8rb",
        "namespace": "monitoring",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/monitoring/pods/custom-metrics-apiserver-657644489c-pk8rb",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:04Z",
      "window": "30s",
      "containers": [
        {
          "name": "custom-metrics-apiserver",
          "usage": {
            "cpu": "22409370n",
            "memory": "42468Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "kube2iam-qghgt",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube2iam-qghgt",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:11Z",
      "window": "30s",
      "containers": [
        {
          "name": "kube2iam",
          "usage": {
            "cpu": "2078992n",
            "memory": "16356Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "spot-interrupt-handler-ps745",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/spot-interrupt-handler-ps745",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:10Z",
      "window": "30s",
      "containers": [
        {
          "name": "spot-interrupt-handler",
          "usage": {
            "cpu": "611566n",
            "memory": "4336Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "coredns-68fb7946fb-2xnpp",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/coredns-68fb7946fb-2xnpp",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:12Z",
      "window": "30s",
      "containers": [
        {
          "name": "coredns",
          "usage": {
            "cpu": "1610381n",
            "memory": "10480Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "coredns-68fb7946fb-9ctjf",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/coredns-68fb7946fb-9ctjf",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:13Z",
      "window": "30s",
      "containers": [
        {
          "name": "coredns",
          "usage": {
            "cpu": "1418850n",
            "memory": "9852Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "prometheus-7d4f6d4454-v4fnd",
        "namespace": "monitoring",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/monitoring/pods/prometheus-7d4f6d4454-v4fnd",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:00Z",
      "window": "30s",
      "containers": [
        {
          "name": "prometheus",
          "usage": {
            "cpu": "17951807n",
            "memory": "202316Ki"
          }
        }
      ]
    },
    {
      "metadata": {
        "name": "metrics-server-7cdd54ccb4-k2x7m",
        "namespace": "kube-system",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/metrics-server-7cdd54ccb4-k2x7m",
        "creationTimestamp": "2019-02-13T08:35:19Z"
      },
      "timestamp": "2019-02-13T08:35:04Z",
      "window": "30s",
      "containers": [
        {
          "name": "metrics-server-nanny",
          "usage": {
            "cpu": "144656n",
            "memory": "5716Ki"
          }
        },
        {
          "name": "metrics-server",
          "usage": {
            "cpu": "568327n",
            "memory": "16268Ki"
          }
        }
      ]
    }
  ]
}

基于CPU和内存使用情况的Auto Scaling

您将使用基于Golang的小型Web应用程序来测试Horizo​​ntal Pod Autoscaler（HPA）。

将podinfo部署到默认命名空间：

kubectl create -f ./podinfo/podinfo-svc.yaml,./podinfo/podinfo-dep.yaml

使用NodePort服务访问podinfo，地址为http：// <K8S_PUBLIC_IP>：31198。

接下来定义一个至少维护两个副本的HPA，如果CPU平均值超过80％或内存超过200Mi，则最多可扩展到10个：

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: podinfo
spec:
  scaleTargetRef:
    apiVersion: extensions/v1beta1
    kind: Deployment
    name: podinfo
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 80
  - type: Resource
    resource:
      name: memory
      targetAverageValue: 200Mi

创建这个hpa:

kubectl create -f ./podinfo/podinfo-hpa.yaml

几秒钟后，HPA控制器联系度量服务器，然后获取CPU和内存使用情况：

kubectl get hpa

NAME      REFERENCE            TARGETS                      MINPODS   MAXPODS   REPLICAS   AGE
podinfo   Deployment/podinfo   2826240 / 200Mi, 15% / 80%   2         10        2          5m

为了增加CPU使用率，请使用rakyll / hey运行负载测试：

#install hey
go get -u github.com/rakyll/hey

#do 10K requests
hey -n 10000 -q 10 -c 5 http://<K8S_PUBLIC_IP>:31198/

您可以使用以下方式监控HPA事件：

$ kubectl describe hpa

Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  7m    horizontal-pod-autoscaler  New size: 4; reason: cpu resource utilization (percentage of request) above target
  Normal  SuccessfulRescale  3m    horizontal-pod-autoscaler  New size: 8; reason: cpu resource utilization (percentage of request) above target

暂时删除podinfo。稍后将在本教程中再次部署它：

kubectl delete -f ./podinfo/podinfo-hpa.yaml,./podinfo/podinfo-dep.yaml,./podinfo/podinfo-svc.yaml

部署 Custom Metrics Server

要根据自定义指标进行扩展，您需要拥有两个组件。一个组件，用于从应用程序收集指标并将其存储在Prometheus时间序列数据库中。第二个组件使用collect（ k8s-prometheus-adapter ）提供的指标扩展了Kubernetes自定义指标API。

k8s与HPA--通过 Prometheus adaptor 来自定义监控指标

您将在专用命名空间中部署Prometheus和适配器。

创建 monitoring 命名空间： 

kubectl create -f ./namespaces.yaml

在 monitoring 命名空间中部署Prometheus v2： 

kubectl create -f ./prometheus

生成Prometheus适配器所需的TLS证书：

make certs

生成以下几个文件：

# ls output
apiserver.csr  apiserver-key.pem  apiserver.pem

部署Prometheus自定义指标API适配器：

kubectl create -f ./custom-metrics-api

列出Prometheus提供的自定义指标：

kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1" | jq .

获取 monitoring 命名空间中所有pod的FS使用情况： 

kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/monitoring/pods/*/fs_usage_bytes" | jq .

查询结果如下：

{
  "kind": "MetricValueList",
  "apiVersion": "custom.metrics.k8s.io/v1beta1",
  "metadata": {
    "selfLink": "/apis/custom.metrics.k8s.io/v1beta1/namespaces/monitoring/pods/%2A/fs_usage_bytes"
  },
  "items": [
    {
      "describedObject": {
        "kind": "Pod",
        "namespace": "monitoring",
        "name": "custom-metrics-apiserver-657644489c-pk8rb",
        "apiVersion": "/v1"
      },
      "metricName": "fs_usage_bytes",
      "timestamp": "2019-02-13T08:52:30Z",
      "value": "94253056"
    },
    {
      "describedObject": {
        "kind": "Pod",
        "namespace": "monitoring",
        "name": "prometheus-7d4f6d4454-v4fnd",
        "apiVersion": "/v1"
      },
      "metricName": "fs_usage_bytes",
      "timestamp": "2019-02-13T08:52:30Z",
      "value": "24576"
    }
  ]
}

基于custom metrics 自动伸缩

在默认命名空间中创建podinfo NodePort服务和部署：

kubectl create -f ./podinfo/podinfo-svc.yaml,./podinfo/podinfo-dep.yaml

podinfo应用程序公开名为http_requests_total的自定义指标。 Prometheus适配器删除_total后缀并将度量标记为计数器度量标准。

从自定义指标API获取每秒的总请求数：

kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/http_requests" | jq .
{
  "kind": "MetricValueList",
  "apiVersion": "custom.metrics.k8s.io/v1beta1",
  "metadata": {
    "selfLink": "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/%2A/http_requests"
  },
  "items": [
    {
      "describedObject": {
        "kind": "Pod",
        "namespace": "default",
        "name": "podinfo-6b86c8ccc9-kv5g9",
        "apiVersion": "/__internal"
      },
      "metricName": "http_requests",
      "timestamp": "2018-01-10T16:49:07Z",
      "value": "901m"
    },
    {
      "describedObject": {
        "kind": "Pod",
        "namespace": "default",
        "name": "podinfo-6b86c8ccc9-nm7bl",
        "apiVersion": "/__internal"
      },
      "metricName": "http_requests",
      "timestamp": "2018-01-10T16:49:07Z",
      "value": "898m"
    }
  ]
}

建一个HPA，如果请求数超过每秒10个，将扩展podinfo部署：

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: podinfo
spec:
  scaleTargetRef:
    apiVersion: extensions/v1beta1
    kind: Deployment
    name: podinfo
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metricName: http_requests
      targetAverageValue: 10

在默认命名空间中部署podinfo HPA：

kubectl create -f ./podinfo/podinfo-hpa-custom.yaml

几秒钟后，HPA从指标API获取http_requests值：

kubectl get hpa

NAME      REFERENCE            TARGETS     MINPODS   MAXPODS   REPLICAS   AGE
podinfo   Deployment/podinfo   899m / 10   2         10        2          1m

在podinfo服务上应用一些负载，每秒25个请求：

#install hey
go get -u github.com/rakyll/hey

#do 10K requests rate limited at 25 QPS
hey -n 10000 -q 5 -c 5 http://<K8S-IP>:31198/healthz

几分钟后，HPA开始扩展部署：

kubectl describe hpa

Name:                       podinfo
Namespace:                  default
Reference:                  Deployment/podinfo
Metrics:                    ( current / target )
  "http_requests" on pods:  9059m / 10
Min replicas:               2
Max replicas:               10

Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  2m    horizontal-pod-autoscaler  New size: 3; reason: pods metric http_requests above target

按照当前的每秒请求速率，部署永远不会达到10个pod的最大值。三个复制品足以使每个吊舱的RPS保持在10以下。

负载测试完成后，HPA会将部署缩到其初始副本：

Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  5m    horizontal-pod-autoscaler  New size: 3; reason: pods metric http_requests above target
  Normal  SuccessfulRescale  21s   horizontal-pod-autoscaler  New size: 2; reason: All metrics below target

您可能已经注意到自动缩放器不会立即对使用峰值做出反应。默认情况下，度量标准同步每30秒发生一次，只有在最后3-5分钟内没有重新缩放时才能进行扩展/缩小。通过这种方式，HPA可以防止快速执行冲突的决策，并为Cluster Autoscaler提供时间。

结论

并非所有系统都可以通过单独依赖CPU /内存使用指标来满足其SLA，大多数Web和移动后端需要基于每秒请求进行自动扩展以处理任何流量突发。对于ETL应用程序，可以通过作业队列长度超过某个阈值等来触发自动缩放。通过使用Prometheus检测应用程序并公开正确的自动缩放指标，您可以对应用程序进行微调，以更好地处理突发并确保高可用性。


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