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# 一、HPA控制器简介
HPA（Horizontal Pod Autoscaler）控制器可以获取pod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与之前的Deployment一样，也属于一种Kubernetes对象，它通过追踪分析目标pod的负载变化情况，类确定是否需要针对性地调整目标pod的副本数
![](/static/upload/20220523_140008.png)

# 二、HPA控制器环境准备
## 2.1 安装metric-server
（1）参照 [Git----安装(CentOS)](https://blog.csdn.net/redrose2100/article/details/121007604) 首先安装git工具
（2）下载metric-server
```bash
git clone -b v0.3.6 https://github.com/kubernetes-incubator/metrics-server
```
（3）配置yaml文件
```bash
cd metrics-server/deploy/1.8+/
vi metrics-server-deployment.yaml
```
然后按照下图的位置编辑文件，内容如下：
```bash
hostNetwork: true
image: registry.cn-hangzhou.aliyuncs.com/google_containers/metrics-server-amd64:v0.3.6
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP,Hostname,InternalNDS,ExternalDNS,ExternalIP
```

![在这里插入图片描述](https://img-blog.csdnimg.cn/d126a310dc8a405aab40e4d87a56c2d4.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAcmVkcm9zZTIxMDA=,size_20,color_FFFFFF,t_70,g_se,x_16)
（4）使用如下命令部署
```bash
[root@master 1.8+]# kubectl apply -f ./
clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created
Warning: rbac.authorization.k8s.io/v1beta1 ClusterRoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 ClusterRoleBinding
clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created
Warning: rbac.authorization.k8s.io/v1beta1 RoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 RoleBinding
rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created
Warning: apiregistration.k8s.io/v1beta1 APIService is deprecated in v1.19+, unavailable in v1.22+; use apiregistration.k8s.io/v1 APIService
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
[root@master 1.8+]#
```
查询如下，有metrics-server-669dfc56ff-v6drv  即表示已经部署成功了
```bash
[root@master 1.8+]# kubectl get pod -n kube-system
NAME                              READY   STATUS    RESTARTS   AGE
coredns-558bd4d5db-7vbmq          1/1     Running   0          12d
coredns-558bd4d5db-sps22          1/1     Running   0          12d
etcd-master                       1/1     Running   0          12d
kube-apiserver-master             1/1     Running   0          12d
kube-controller-manager-master    1/1     Running   0          12d
kube-flannel-ds-cd9qk             1/1     Running   0          12d
kube-flannel-ds-gg4jq             1/1     Running   0          12d
kube-flannel-ds-n76xj             1/1     Running   0          12d
kube-proxy-g4j5g                  1/1     Running   0          12d
kube-proxy-h27ms                  1/1     Running   0          12d
kube-proxy-tqzjl                  1/1     Running   0          12d
kube-scheduler-master             1/1     Running   0          12d
metrics-server-669dfc56ff-v6drv   1/1     Running   0          56s
[root@master 1.8+]#
```
（5）此时通过如下命令，可以查询到节点的资源使用情况
```bash
[root@master 1.8+]# kubectl top node
W0327 11:53:58.289701    9379 top_node.go:119] Using json format to get metrics. Next release will switch to protocol-buffers, switch early by passing --use-protocol-buffers flag
NAME     CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%
master   71m          0%     1268Mi          3%
node1    24m          0%     687Mi           2%
node2    23m          0%     721Mi           2%
[root@master 1.8+]#
```
（6）通过如下命令，可以查看到pod的资源使用情况
```bash
[root@master 1.8+]# kubectl top  pod -n kube-system
W0327 11:55:35.833234   10195 top_pod.go:140] Using json format to get metrics. Next release will switch to protocol-buffers, switch early by passing --use-protocol-buffers flag
NAME                              CPU(cores)   MEMORY(bytes)
coredns-558bd4d5db-7vbmq          1m           13Mi
coredns-558bd4d5db-sps22          1m           13Mi
etcd-master                       9m           26Mi
kube-apiserver-master             22m          297Mi
kube-controller-manager-master    6m           53Mi
kube-flannel-ds-cd9qk             2m           15Mi
kube-flannel-ds-gg4jq             2m           15Mi
kube-flannel-ds-n76xj             1m           16Mi
kube-proxy-g4j5g                  1m           19Mi
kube-proxy-h27ms                  1m           19Mi
kube-proxy-tqzjl                  1m           19Mi
kube-scheduler-master             2m           21Mi
metrics-server-669dfc56ff-v6drv   1m           15Mi
[root@master 1.8+]#
```
## 2.2 创建deployment和service
编辑service_deployment.yaml文件，内容如下：
```yaml
apiVersion: v1
kind: Namespace
metadata:
  name: dev

---

apiVersion: apps/v1
kind: Deployment
metadata:
  name: deploy-nginx
  namespace: dev
spec:
  replicas: 1
  selector:
    matchLabels:
      run: nginx
  template:
    metadata:
      labels:
        run: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.17.1
        ports:
        - containerPort: 80
          protocol: TCP

---

apiVersion: v1
kind: Service
metadata:
  name: service-nginx
  namespace: dev
spec:
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
    nodePort: 30030
  selector:
    run: nginx
  type: NodePort
```
使用如下命令创建
```bash
[root@master pod_controller]# kubectl apply -f service_deployment.yaml
namespace/dev created
deployment.apps/deploy-nginx created
service/service-nginx created
[root@master pod_controller]#
```
查看创建的资源如下：
```bash
[root@master pod_controller]# kubectl get deployment,pod,service -n dev
NAME                           READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/deploy-nginx   3/3     3            3           5m6s

NAME                                READY   STATUS    RESTARTS   AGE
pod/deploy-nginx-66ffc897cf-jhc9w   1/1     Running   0          5m6s

NAME                    TYPE       CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
service/service-nginx   NodePort   10.106.128.172   <none>        80:30030/TCP   5m6s
[root@master pod_controller]#
```
## 2.3 部署HPA
编辑pc_hpa.yaml文件，内容如下：注意这里把cpu使用率设置为1%是为了测试用。在实际部署中需要根据具体情况去定
```yaml
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: pc-hpa
  namespace: dev
spec:
  minReplicas: 1
  maxReplicas: 10
  targetCPUUtilizationPercentage: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: deploy-nginx
```
然后使用如下命令创建
```bash
[root@master pod_controller]# kubectl apply -f pc_hpa.yaml
horizontalpodautoscaler.autoscaling/pc-hpa created
[root@master pod_controller]#
```
查看hpa如下
```bash
[root@master pod_controller]# kubectl get hpa -n dev
NAME     REFERENCE                 TARGETS        MINPODS   MAXPODS   REPLICAS   AGE
pc-hpa   Deployment/deploy-nginx   <unknown>/1%   1         10        1          5m18s
[root@master pod_controller]#
```
## 2.4 压力测试观测HPA控制器
开三个窗口分别对pod，deployment，hpa进行监视分别使用如下命令
```bash
kubectl get pod -n dev -w

kubectl get deploy -n dev -w

kubectl get hpa -n dev -w
```
编写测试脚本 test.sh，用于发送get请求
```bash
#! /bin/bash

for((i=1;i<1000000;i++));
do
curl http://192.168.16.40:30030;
done
```
执行测试脚本，然后观察pod数量变化

如下为deploy的观测结果：
```bash
[root@master ~]# kubectl get deploy -n dev -w
NAME           READY   UP-TO-DATE   AVAILABLE   AGE
deploy-nginx   1/1     1            1           82m
deploy-nginx   1/3     1            1           84m
deploy-nginx   1/3     1            1           84m
deploy-nginx   1/3     1            1           84m
deploy-nginx   1/3     3            1           84m
deploy-nginx   2/3     3            2           84m
deploy-nginx   3/3     3            3           84m
```
如下为HPA的观测结果
```bash
[root@master ~]# kubectl get hpa -n dev -w
NAME     REFERENCE                 TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
pc-hpa   Deployment/deploy-nginx   0%/1%     1         10        1          63m
pc-hpa   Deployment/deploy-nginx   3%/1%     1         10        1          64m
pc-hpa   Deployment/deploy-nginx   3%/1%     1         10        3          64m
pc-hpa   Deployment/deploy-nginx   1%/1%     1         10        3          65m
pc-hpa   Deployment/deploy-nginx   1%/1%     1         10        3          66m
```
可以看到，HPA确实可以做到动态扩缩容

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