Kubernetes学习指南(三)–Pod水平自动伸缩
Pod 水平自动伸缩（Horizontal Pod Autoscaler）特性， 可以基于CPU利用率自动伸缩 replication controller、deployment和 replica set 中的 pod 数量，（除了 CPU 利用率）也可以 基于其他应程序提供的度量指标
安装指标收集组件Metrics server
下载项目
## 没有安装 git 要先安装
# yum install git -y
git clone https://github.com/kubernetes-sigs/metrics-server.git
添加参数
vim metrics-server/deploy/1.8+/metrics-server-deployment.yaml
containers:
- name: metrics-server
image: k8s.gcr.io/metrics-server-amd64:v0.3.6
## 添加下面几行
command:
- /metrics-server
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP,Hostname,InternalDNS,ExternalDNS,ExternalIP
- --v=2
开始安装
# kubectl apply -f metrics-server/deploy/1.8+/
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
或者直接使用修改完成的版本
kubectl apply -f https://raw.githubusercontent.com/charSLee013/Kubernetes-learn/master/chapter03/metrics-server-1.8.yaml
查看机器的负载情况
#kubectl top node
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
master001 131m 13% 1166Mi 67%
node1 78m 7% 922Mi 53%
node2 80m 8% 934Mi 53%
创建自动扩容和缩容Pod
下面通过为一个Deployment设置HPA,然后使用一个客户端为其进行压力测试,对HPA的用法进行实例
以php-apache的Deployment为例,设置 cpu request = 200m,未设置 limit 上限的值
cat <<EOF > php-apache-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: php-apache
spec:
replicas: 1
selector:
matchLabels:
app: php-apache
template:
metadata:
name: php-apache
labels:
app: php-apache
spec:
containers:
- image: k8s.gcr.io/hpa-example
name: php-apache
ports:
- containerPort: 80
protocol: TCP
resources:
requests:
cpu: 200m
EOF
kubectl apply -f php-apache-deployment.yaml
再创建一个php-apache的Service,供客户端访问
cat <<EOF >php-apache-service.yaml
apiVersion: v1
kind: Service
metadata:
name: php-apache
spec:
ports:
- port: 80
selector:
app: php-apache
EOF
kubectl apply -f php-apache-service.yaml
创建一个HPA控制器
使 Pod 的副本数量在维持在1到10之间 大致来说，HPA 将通过增加或者减少 Pod 副本的数量（通过 Deployment ）以保持所有 Pod 的平均CPU利用率在50%以内
cat <<EOF >php-apache-hpa.yaml
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: php-apache
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
EOF
kubectl apply -f php-apache-hpa.yaml
查看已经创建的HPA
# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache <unknown>/50% 1 10 0 10s
创建前30秒CPU指标都是 metrics-server 是否运行成功
小提示
## 下面这条命令可以实时监控HPA负载情况
watch -n 1 kubectl get hpa
开始负载
另外启动一个容器，并通过一个循环向 php-apache 服务器发送无限的查询请求
kubectl run -i --tty load-generator --image=busybox /bin/sh
while true; do wget -q -O- http://php-apache; done
或者直接对Service的虚拟ClusterIP地址进行访问
# kubectl get svc php-apache
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
php-apache ClusterIP 10.96.173.5 <none> 80/TCP 21m
## 增加负载
while true; do wget -q -O- http://10.96.173.5; done
过几分钟后,可以观察到HPA控制器收集到的Pod CPU使用率
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache 448%/50% 1 10 1 23m
再过一会查看下php-apache的副本数量变化
# kubectl get deployment php-apache
NAME READY UP-TO-DATE AVAILABLE AGE
php-apache 6/9 9 6 3m18s
可以查看HPA已经根据Pod的CPU使用率的提高而进行Pod自动扩容
停止负载
停止负载来结束我们的示例. 输入
# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache 1%/50% 1 10 1 31m
可以看到HPA根据Pod CPU使用率的降低对副本数量进行了缩容操作
一键安装metrics server命令
kubectl apply -f https://raw.githubusercontent.com/charSLee013/Kubernetes-learn/master/chapter03/metrics-server-1.8.yaml
一键构建测试环境
kubectl apply -f https://raw.githubusercontent.com/charSLee013/Kubernetes-learn/master/chapter03/test-php-apache-hpa.yaml
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