HPA

HorizontalPodAutoscaler（简称 HPA ）自动化伸缩策略，可以根据服务负载情况自动扩展或缩小实例数量，以应对不同的流量负载。

实践

1、你需要先一个部署并配置了 Metrics Server 的集群。 Kubernetes Metrics Server 从集群中的 kubelets 收集资源指标， 并通过 Kubernetes API 公开这些指标， 使用 APIService 添加代表指标读数的新资源。

kubectl apply -f metrics-server-components.yaml

此metrics-server-components.yaml文件内容为：

apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server

  namespace: kube-system
---

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
    rbac.authorization.k8s.io/aggregate-to-admin: "true"
    rbac.authorization.k8s.io/aggregate-to-edit: "true"
    rbac.authorization.k8s.io/aggregate-to-view: "true"
  name: system:aggregated-metrics-reader
rules:

- apiGroups:
  - metrics.k8s.io
    resources:
  - pods
  - nodes
    verbs:
  - get
  - list
  - watch

---

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
rules:

- apiGroups:
  - ""
    resources:
  - nodes/metrics
    verbs:
  - get
- apiGroups:
  - ""
    resources:
  - pods
  - nodes
    verbs:
  - get
  - list
  - watch

---

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server-auth-reader
  namespace: kube-system
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: extension-apiserver-authentication-reader
subjects:

- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system

---

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server:system:auth-delegator
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:auth-delegator
subjects:

- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system

---

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:metrics-server
subjects:

- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system

---

apiVersion: v1
kind: Service
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  ports:

  - name: https
    port: 443
    protocol: TCP
    targetPort: https
      selector:
    k8s-app: metrics-server

---

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  selector:
    matchLabels:
      k8s-app: metrics-server
  strategy:
    rollingUpdate:
      maxUnavailable: 0
  template:
    metadata:
      labels:
        k8s-app: metrics-server
    spec:
      hostNetwork: true
      containers:
      - args:
        - --kubelet-insecure-tls
        - --cert-dir=/tmp
        - --secure-port=4443
        - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
        - --kubelet-use-node-status-port
        - --metric-resolution=15s
        image: registry.cn-hangzhou.aliyuncs.com/google_containers/metrics-server:v0.6.2
        imagePullPolicy: IfNotPresent
        livenessProbe:
          failureThreshold: 3
          httpGet:
            path: /livez
            port: https
            scheme: HTTPS
          periodSeconds: 10
        name: metrics-server
        ports:
        - containerPort: 4443
          name: https
          protocol: TCP
        readinessProbe:
          failureThreshold: 3
          httpGet:
            path: /readyz
            port: https
            scheme: HTTPS
          initialDelaySeconds: 20
          periodSeconds: 10
        resources:
          requests:
            cpu: 100m
            memory: 200Mi
        securityContext:
          allowPrivilegeEscalation: false
          readOnlyRootFilesystem: true
          runAsNonRoot: true
          runAsUser: 1000
        volumeMounts:
        - mountPath: /tmp
          name: tmp-dir
      nodeSelector:
        kubernetes.io/os: linux
      priorityClassName: system-cluster-critical
      serviceAccountName: metrics-server
      volumes:
      - emptyDir: {}

        name: tmp-dir
---

apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  labels:
    k8s-app: metrics-server
  name: v1beta1.metrics.k8s.io
spec:
  group: metrics.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: metrics-server
    namespace: kube-system
  version: v1beta1
  versionPriority: 100

测试

创建成功。

2、创建一个Deployment或ReplicaSet的HAP资源，以定义需要自动伸缩的服务目标。

apiVersion: apps/v1
kind: Deployment
metadata:
  name: php-apache
spec:
  selector:
    matchLabels:
      run: php-apache
  replicas: 1
  template:
    metadata:
      labels:
        run: php-apache
    spec:
      containers:
      - name: php-apache
        image: harbor/library/hpa-example
        ports:
        - containerPort: 80
        resources:
          limits:
            cpu: 500m
          requests:
            cpu: 200m
---
apiVersion: v1
kind: Service
metadata:
  name: php-apache
  labels:
    run: php-apache
spec:
  ports:
  - port: 80
  selector:
    run: php-apache

dockerfile文件

FROM php:5-apache
COPY index.php /var/www/html/index.php
RUN chmod a+rx index.php

index.php文件（定义一个 index.php 页面来执行一些 CPU 密集型计算）：

<?php
  $x = 0.0001;
  for ($i = 0; $i <= 1000000; $i++) {
    $x += sqrt($x);
  }
  echo "OK!";
?>

构建镜像：docker build -t harbor/library/hpa-example .
推送镜像：docker push harbor/library/hpa-example

[root@master ]# kubectl apply -f php-apache.yaml
deployment.apps/php-apache created
service/php-apache created

3、现在服务器正在运行，使用 kubectl 创建自动扩缩器。

kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10

命令：cpu平均值超过50%，最少pod数为1，最大pod数为10
查看

[root@master ~]# kubectl get hpa
NAME         REFERENCE               TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache   0%/50%    1         10        1          80m

请注意当前的 CPU 利用率是 0%，这是由于我们尚未发送任何请求到服务器 TARGET 列显示了相应 Deployment 所控制的所有 Pod 的平均 CPU 利用率

4、接下来，看看自动扩缩器如何对增加的负载做出反应。 这边将启动一个不同的 Pod 作为客户端。 客户端 Pod 中的容器在无限循环中运行，向 php-apache 服务发送查询。

另一个终端查看

ctrl+c 停止负载测试之后，cpu负载慢慢下降：可以看到他会缩容，pod数量会减少

一旦 CPU 利用率降至 0，HPA 会自动将副本数缩减为 1。
自动扩缩完成副本数量的改变可能需要几分钟的时间。