# Horizontal Pod Autoscaler

我们已经可以通过手动执行 kubectl scale命令实现 Pod 的扩缩容，但是这显然不符合 k8s 的定位目标：自动化和智能化。k8s 期望可以通过监测 Pod 的使用情况，实现 Pod 数量的自动调整，于是就产生了 HPA 这种控制器。

HPA 可以获取每个 Pod 的利用率，然后和 HPA 中定义的指标进行对比，同时计算出需要伸缩的具体值，最后实现 Pod 的数量的调整。其实 HPA 和之前的 Deployment 一样，也属于一种 k8s 资源对象，它通过追踪分析目标 Pod 的负载变化情况，来确定是否需要针对性的调整目标 Pod 的副本数。

# 一、安装 metrics-server（v0.3.6）

下载 metrics-server

wget https://github.com/kubernetes-sigs/metrics-server/archive/v0.3.6.tar.gz

解压
```
tar -zxvf v0.3.6.tar.gz
```
进入 metrics-server-0.3.6/deploy/1.8+ 目录
```
cd metrics-server-0.3.6/deploy/1.8+
```

修改 metrics-server-deployment.yaml 文件：

---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: metrics-server
  namespace: kube-system
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: metrics-server
  namespace: kube-system
  labels:
    k8s-app: metrics-server
spec:
  selector:
    matchLabels:
      k8s-app: metrics-server
  template:
    metadata:
      name: metrics-server
      labels:
        k8s-app: metrics-server
    spec:
      # 添加
      hostNetwork: true
      serviceAccountName: metrics-server
      volumes:
      - name: tmp-dir
        emptyDir: {}
      containers:
      - name: metrics-server
        # 修改
        image: registry.cn-hangzhou.aliyuncs.com/google_containers/metrics-server-amd64:v0.3.6
        imagePullPolicy: Always
        # 添加
        args:
         - --kubelet-insecure-tls 
         - --kubelet-preferred-address-types=InternalIP,Hostname,InternalDNS,ExternalDNS,ExternalIP
        volumeMounts:
        - name: tmp-dir
          mountPath: /tmp

安装 metrics-server

[root@k8s-master 1.8+]# kubectl apply -f ./
clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created
clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created
rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created
apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created
serviceaccount/metrics-server created
deployment.apps/metrics-server created
service/metrics-server created
clusterrole.rbac.authorization.k8s.io/system:metrics-server created
clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created

查看 metrics-server 生成的 Pod

[root@k8s-master 1.8+]# kubectl get pod -n kube-system
NAME                                 READY   STATUS    RESTARTS   AGE
coredns-66bff467f8-7n949             1/1     Running   3          24h
coredns-66bff467f8-knb4n             1/1     Running   1          23h
etcd-k8s-master                      1/1     Running   4          5d23h
kube-apiserver-k8s-master            1/1     Running   1          23h
kube-controller-manager-k8s-master   1/1     Running   7          5d23h
kube-flannel-ds-l5jfm                1/1     Running   4          5d23h
kube-flannel-ds-rjxpl                1/1     Running   0          23h
kube-flannel-ds-vpbd9                1/1     Running   0          23h
kube-proxy-9z64b                     1/1     Running   0          23h
kube-proxy-j9pkd                     1/1     Running   4          5d23h
kube-proxy-znbcm                     1/1     Running   2          5d23h
kube-scheduler-k8s-master            1/1     Running   2          23h
metrics-server-5f55b696bd-9hg5w      1/1     Running   0          2m3s   #这个就是

查看资源使用情况

[root@k8s-master 1.8+]# kubectl top pod -n kube-system
NAME                                 CPU(cores)   MEMORY(bytes)   
coredns-66bff467f8-7n949             3m           11Mi            
coredns-66bff467f8-knb4n             3m           8Mi             
etcd-k8s-master                      19m          58Mi            
kube-apiserver-k8s-master            45m          375Mi           
kube-controller-manager-k8s-master   17m          68Mi            
kube-flannel-ds-l5jfm                3m           18Mi            
kube-flannel-ds-rjxpl                3m           15Mi            
kube-flannel-ds-vpbd9                3m           16Mi            
kube-proxy-9z64b                     1m           19Mi            
kube-proxy-j9pkd                     1m           16Mi            
kube-proxy-znbcm                     1m           19Mi            
kube-scheduler-k8s-master            4m           26Mi            
metrics-server-5f55b696bd-9hg5w      1m           10Mi

# 二、准备 Deployment

编写 nginx.yaml 文件

apiVersion: apps/v1 
kind: Deployment 
metadata: 
  name: nginx
spec: 
  selector: 
    matchLabels: 
      app: nginx-pod
  template: 
    metadata:
      labels:
        app: nginx-pod
    spec:
      containers:
        - name: nginx 
          image: nginx:1.17.1 
          ports:
            - containerPort: 80 
          resources: # 资源限制
            requests:
              cpu: "100m" # 100m表示100millicpu，即0.1个CPU

创建 Deployment

[root@k8s-master 1.8+]# kubectl apply -f nginx-hpa.yaml 
deployment.apps/nginx-hpa created

# 三、准备 Service

kubectl expose deployment nginx --name=nginx --type=NodePort --port=80 --target-port=80

# 四、部署 HPA

创建 ps-hpa.yaml 文件

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: pc-hpa
spec:
  minReplicas: 1 #最小Pod数量
  maxReplicas: 10 #最大Pod数量
  targetCPUUtilizationPercentage: 3 #CPU使用率指标
  scaleTargetRef: #指定要控制的Nginx信息
    apiVersion: apps/v1
    kind: Deployment
    name: nginx #要和前面的Deployment名称对应上

创建 hpa

[root@k8s-master 1.8+]# kubectl create -f pc-hpa.yaml 
horizontalpodautoscaler.autoscaling/pc-hpa created

查看 hpa

[root@k8s-master 1.8+]# kubectl get hpa
NAME     REFERENCE              TARGETS        MINPODS   MAXPODS   REPLICAS   AGE
pc-hpa   Deployment/nginx   <unknown>/3%   1         10        1          39s

# 五、测试

查看 Service

[root@k8s-master 1.8+]# kubectl get service nginx
NAME        TYPE       CLUSTER-IP     EXTERNAL-IP   PORT(S)        AGE
nginx      NodePort   10.96.88.161   <none>        80:31395/TCP   42s

使用压测工具 Jmeter 对 service 的地址 http://172.16.58.200:31395 进行压测，然后通过控制台查看 hpa 和 pod 的变化
HPA 的变化
Pod 的变化

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