kubernetes学习笔记之十二：资源指标API及自定义指标API
第一章、前言
以前是用heapster来收集资源指标才能看，现在heapster要废弃了
从1.8以后引入了资源api指标监视
资源指标：metrics-server(核心指标)
自定义指标：prometheus，k8s-prometheus-adapter(将Prometheus采集的数据转换为指标格式)
k8s的中的prometheus需要k8s-prometheus-adapter转换一下才可以使用
新一代架构：
核心指标流水线：
kubelet，metrics-service以及API service提供api组成；cpu累计使用率，内存实时使用率，pod的资源占用率和容器磁盘占用率；
监控流水线：
用于从系统收集各种指标数据并提供终端用户，存储系统以及HPA，他们包括核心指标以及很多非核心指标，非核心指标本身不能被k8s解析
第二章、安装部署metrics-server
1、下载yaml文件,并安装
项目地址：https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/metrics-server，选择与版本对应的分支，我的是v1.10.0,所以这里我选择v1.10.0分支
[root@k8s-master_01 manifests]# mkdir metrics-server
[root@k8s-master_01 manifests]# cd metrics-server
[root@k8s-master_01 metrics-server]# for file in auth-delegator.yaml auth-reader.yaml metrics-apiservice.yaml metrics-server-deployment.yaml metrics-server-service.yaml resource-reader.yaml;do wget https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/cluster/addons/metrics-server/$file;done #记住，下载raw格式的文件
[root@k8s-master_01 metrics-server]# grep image: ./* #查看使用的镜像，如果可以科学上网，那么忽略，如果不可用那么需要提前下载，通过修改配置文件或修改镜像的名称的方式加载镜像，镜像可以到阿里云上去搜索
./metrics-server-deployment.yaml: image: k8s.gcr.io/metrics-server-amd64:v0.2.1
./metrics-server-deployment.yaml: image: k8s.gcr.io/addon-resizer:1.8.1
[root@k8s-node_01 ~]# docker pull registry.cn-hangzhou.aliyuncs.com/criss/addon-resizer:1.8.1 #手动在所有的node节点上下载镜像，注意版本号没有v
[root@k8s-node_01 ~]# docker pull registry.cn-hangzhou.aliyuncs.com/k8s-kernelsky/metrics-server-amd64:v0.2.1
[root@k8s-master_01 metrics-server]# grep image: metrics-server-deployment.yaml
image: registry.cn-hangzhou.aliyuncs.com/k8s-kernelsky/metrics-server-amd64:v0.2.1
image: registry.cn-hangzhou.aliyuncs.com/criss/addon-resizer:1.8.1
[root@k8s-master_01 metrics-server]# kubectl apply -f .
[root@k8s-master_01 metrics-server]# kubectl get pod -n kube-system
2、验证
[root@k8s-master01 ~]# kubectl api-versions |grep metrics
metrics.k8s.io/v1beta1
[root@k8s-node01 ~]# kubectl proxy --port= #重新打开一个终端，启动代理功能
[root@k8s-master_01 metrics-server]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1 #查看这个资源组包含哪些组件
[root@k8s-master_01 metrics-server]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/pods #可能需要等待一会在会有数据
[root@k8s-master_01 metrics-server]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes
[root@k8s-node01 ~]# kubectl top node
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
k8s-master01 176m % 3064Mi %
k8s-node01 62m % 4178Mi %
k8s-node02 65m % 2141Mi %
[root@k8s-node01 ~]# kubectl top pods
NAME CPU(cores) MEMORY(bytes)
node-affinity-pod 0m 1Mi
3.注意事项
1.#在更新的版本中，如v1.11及以上会出现问题，这是因为metric-service默认从kubernetes的summary_api中获取数据，而summary_api默认使用10255端口来获
取数据，但是10255是一个http协议的端口，可能官方认为http协议不安全所以封禁了10255端口改为使用10250端口，而10250是一个https协议端口，所以我们需要修改一下连接方式：
由 - --source=kubernetes.summary_api:''
修改为 - --source=kubernetes.summary_api:https://kubernetes.default?kubeletHttps=true&kubeletPort=10250&insecure-true #表示虽然我使用https协议来通信，并且端口也是10250，但是如果证书不能认证依然可以通过非安全不加密的方式来通信
[root@k8s-node01 deploy]# grep source=kubernetes metrics-server-deployment.yaml
2.[root@k8s-node01 deploy]# grep nodes/stats resource-reader.yaml #在新的版本中，授权文内没有 node/stats 的权限，需要手动去添加
[root@k8s-node01 deploy]# cat resource-reader.yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: system:metrics-server
rules:
- apiGroups:
- ""
resources:
- pods
- nodes
- nodes/stats #添加这一行
- namespaces
3.在1.12.3版本中测试发现，需要进行如下修改才能成功部署(权限依然需要修改，其他版本暂未测试)
[root@k8s-master-01 metrics-server]# vim metrics-server-deployment.yaml
command: #metrics-server命令参数修改为如下参数
- /metrics-server
- --metric-resolution=30s
- --kubelet-port=10250
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP
command: #metrics-server-nanny 命令参数修改为如下参数
- /pod_nanny
- --config-dir=/etc/config
- --cpu=40m
- --extra-cpu=0.5m
- --memory=40Mi
- --extra-memory=4Mi
- --threshold=5
- --deployment=metrics-server-v0.3.1
- --container=metrics-server
- --poll-period=300000
- --estimator=exponential
第三章、安装部署prometheus
项目地址：https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/prometheus(由于prometheus只有v1.11.0及以上才有，所有我选择v1.11.0来部署)
1.下载yaml文件及部署前操作
[root@k8s-node01 ~]# cd /mnt/
[root@k8s-node01 mnt]# git clone https://github.com/kubernetes/kubernetes.git #我嫌麻烦就直接克隆kubernetes整个项目了
[root@k8s-node01 mnt]# cd kubernetes/cluster/addons/prometheus/
[root@k8s-node01 prometheus]# git checkout v1.11.0
[root@k8s-node01 prometheus]# cd ..
[root@k8s-node01 addons]# cp -r prometheus /root/manifests/
[root@k8s-node01 manifests]# cd prometheus/
[root@k8s-node01 prometheus]# grep -w "namespace: kube-system" ./* #默认prometheus使用的是kube-system名称空间，我们把它单独部署到一个名称空间中，方便之后的管理
./alertmanager-configmap.yaml: namespace: kube-system
......
[root@k8s-node01 prometheus]# sed -i 's/namespace: kube-system/namespace\: k8s-monitor/g' ./*
[root@k8s-node01 prometheus]# grep storage: ./* #安装需要两个pv，等下我们需要创建一下
./alertmanager-pvc.yaml: storage: "2Gi"
./prometheus-statefulset.yaml: storage: "16Gi"
[root@k8s-node01 prometheus]# cat pv.yaml #注意第二pv的storageClassName
apiVersion: v1
kind: PersistentVolume
metadata:
name: alertmanager
spec:
capacity:
storage: 5Gi
accessModes:
- ReadWriteOnce
- ReadWriteMany
persistentVolumeReclaimPolicy: Recycle
nfs:
path: /data/volumes/v1
server: 172.16.150.158
---
apiVersion: v1
kind: PersistentVolume
metadata:
name: standard
spec:
capacity:
storage: 25Gi
accessModes:
- ReadWriteOnce
persistentVolumeReclaimPolicy: Recycle
storageClassName: standard #storageClassName与prometheus-statefulset.yaml中volumeClaimTemplates下定义的需要保持一致
nfs:
path: /data/volumes/v2
server: 172.16.150.158
[root@k8s-node01 prometheus]# kubectl create namespace k8s-monitor
[root@k8s-node01 prometheus]# mkdir node-exporter kube-state-metrics alertmanager prometheus #将每个组件单独放入一个目录中，方便部署及管理
[root@k8s-node01 prometheus]# mv node-exporter-* node-exporter
[root@k8s-node01 prometheus]# mv alertmanager-* alertmanager
[root@k8s-node01 prometheus]# mv kube-state-metrics-* kube-state-metrics
[root@k8s-node01 prometheus]# mv prometheus-* prometheus
2.安装node-exporter(用于收集节点的数据指标)
[root@k8s-node01 prometheus]# grep -r image: node-exporter/*
node-exporter/node-exporter-ds.yml: image: "prom/node-exporter:v0.15.2" #非官方镜像，不能科学上网的也可以下载，所以不需要提前下载
[root@k8s-node01 prometheus]# kubectl apply -f node-exporter/
daemonset.extensions "node-exporter" created
service "node-exporter" created
[root@k8s-node01 prometheus]# kubectl get pod -n k8s-monitor
NAME READY STATUS RESTARTS AGE
node-exporter-l5zdw 1/1 Running 0 1m
node-exporter-vwknx 1/1 Running 0 1m
3.安装prometheus
[root@k8s-master_01 prometheus]# kubectl apply -f pv.yaml
persistentvolume "alertmanager" configured
persistentvolume "standard" created
[root@k8s-master_01 prometheus]# kubectl get pv
NAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGE
alertmanager 5Gi RWO,RWX Recycle Available 9s standard 25Gi RWO Recycle Available 9s
[root@k8s-node01 prometheus]# grep -i image prometheus/* #查看镜像是否需要下载
[root@k8s-node01 prometheus]# vim prometheus-service.yaml #默认prometheus的service端口类型为ClusterIP，为了可以集群外访问，修改为NodePort
...
type: NodePort
ports:
- name: http
port: 9090
protocol: TCP
targetPort: 9090
nodePort: 30090
...
[root@k8s-node01 prometheus]# kubectl apply -f prometheus/
[root@k8s-node01 prometheus]# kubectl get pod -n k8s-monitor
NAME READY STATUS RESTARTS AGE
node-exporter-l5zdw 1/1 Running 0 24m
node-exporter-vwknx 1/1 Running 0 24m
prometheus-0 2/2 Running 0 1m
[root@k8s-node01 prometheus]# kubectl get svc -n k8s-monitor
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
node-exporter ClusterIP None <none> 9100/TCP 25m
prometheus NodePort 10.96.9.121 <none> 9090:30090/TCP 22m
[root@k8s-master_01 prometheus]# kubectl get pv
NAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGE
alertmanager 5Gi RWO,RWX Recycle Available 1h
standard 25Gi RWO Recycle Bound k8s-monitor/prometheus-data-prometheus-0 standard 1h
访问prometheus(node节点IP：端口)
4.部署metrics适配器(将prometheus数据转换为k8s可以识别的数据)
[root@k8s-node01 kube-state-metrics]# grep image: ./*
./kube-state-metrics-deployment.yaml: image: quay.io/coreos/kube-state-metrics:v1.3.0
./kube-state-metrics-deployment.yaml: image: k8s.gcr.io/addon-resizer:1.7
[root@k8s-node02 ~]# docker pull registry.cn-hangzhou.aliyuncs.com/ccgg/addon-resizer:1.7
[root@k8s-node01 kube-state-metrics]# vim kube-state-metrics-deployment.yaml #修改镜像地址
[root@k8s-node01 kube-state-metrics]# kubectl apply -f kube-state-metrics-deployment.yaml
deployment.extensions "kube-state-metrics" configured
[root@k8s-node01 kube-state-metrics]# kubectl get pod -n k8s-monitor
NAME READY STATUS RESTARTS AGE
kube-state-metrics-54849b96b4-dmqtk 2/2 Running 0 23s
node-exporter-l5zdw 1/1 Running 0 2h
node-exporter-vwknx 1/1 Running 0 2h
prometheus-0 2/2 Running 0 1h
5.部署k8s-prometheus-adapter(将数据输出为一个API服务)
项目地址：https://github.com/DirectXMan12/k8s-prometheus-adapter
[root@k8s-master01 ~]# cd /etc/kubernetes/pki/
[root@k8s-master01 pki]#(umask 077; openssl genrsa -out serving.key 2048)
[root@k8s-master01 pki]#openssl req -new -key serving.key -out serving.csr -subj "/CN=serving" #CN必须为serving
[root@k8s-master01 pki]#openssl x509 -req -in serving.csr -CA ./ca.crt -CAkey ./ca.key -CAcreateserial -out serving.crt -days 3650
[root@k8s-master01 pki]# kubectl create secret generic cm-adapter-serving-certs --from-file=serving.crt=./serving.crt --from-file=serving.key=./serving.key -n k8s-monitor #证书名称必须为cm-adapter-serving-certs
[root@k8s-master01 pki]#kubectl get secret -n k8s-monitor
[root@k8s-master01 pki]# cd
[root@k8s-node01 ~]# git clone https://github.com/DirectXMan12/k8s-prometheus-adapter.git
[root@k8s-node01 ~]# cd k8s-prometheus-adapter/deploy/manifests/
[root@k8s-node01 manifests]# grep namespace: ./* #处理role-binding之外的namespace的名称改为k8s-monitor
[root@k8s-node01 manifests]# grep image: ./* #镜像不需要下载
[root@k8s-node01 ~]# sed -i 's/namespace\: custom-metrics/namespace\: k8s-monitor/g' ./* #rolebinding的不要替换
[root@k8s-node01 ~]# kubectl apply -f ./
[root@k8s-node01 ~]# kubectl get pod -n k8s-monitor
[root@k8s-node01 ~]#kubectl get svc -n k8s-monitor
kubectl api-versions |grep custom
第四章、部署prometheus+grafana
[root@k8s-master01 ~]# wget https://raw.githubusercontent.com/kubernetes-retired/heapster/master/deploy/kube-config/influxdb/grafana.yaml #找不到grafana的yaml文件，所以到heapster里面掏了一个下来用用
[root@k8s-master01 ~]#egrep -i "influxdb|namespace|nodeport" grafana.yaml #注释掉influxdb环境变量，修改namespace及port类型
[root@k8s-master01 ~]#kubectl apply -f grafana.yaml
[root@k8s-master01 ~]#kubectl get svc -n k8s-monitor
[root@k8s-master01 ~]#kubectl get pod -n k8s-monitor
登录grafana，并修改数据源
配置数据源
点击右侧的Dashborads，可以导入grafana自带的prometheus的模板
回到home下，下拉选择对应的模板查看数据
例如：
但是，grafana自带的模板和数据有些不匹配，我们可以去grafana官网去下载应用于k8s使用的模板，地址为：https://grafana.com/dashboards
访问grafana官网搜索k8s相关模板，有时搜索框点击没有反应，可以直接在URL后面加上搜索内容即可
我们选择kubernetes cluster(prometheus)作为测试
点击需要下载的模板，并下载json文件
下载完成后，导入文件
选择上传文件
导入后选择数据源
导入后展示的界面
第五章、实现HPA
1、使用v1版本测试
[root@k8s-master01 alertmanager]# kubectl api-versions |grep autoscaling
autoscaling/v1
autoscaling/v2beta1
[root@k8s-master01 manifests]# cat deploy-demon.yaml
apiVersion: v1
kind: Service
metadata:
name: myapp
namespace: default
spec:
selector:
app: myapp
type: NodePort
ports:
- name: http
port:
targetPort:
nodePort: ---
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-deploy
spec:
replicas:
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: ikubernetes/myapp:v2
ports:
- name: httpd
containerPort:
resources:
requests:
memory: "64Mi"
cpu: "100m"
limits:
memory: "128Mi"
cpu: "200m"
[root@k8s-master01 manifests]# kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kubernetes ClusterIP 10.96.0.1 <none> /TCP 47d
my-nginx NodePort 10.104.13.148 <none> :/TCP 19d
myapp NodePort 10.100.76.180 <none> :/TCP 16s
tomcat ClusterIP 10.106.222.72 <none> /TCP,/TCP 19d
[root@k8s-master01 manifests]# kubectl get pod
NAME READY STATUS RESTARTS AGE
myapp-deploy-5db497dbfb-h7zcb / Running 16s
myapp-deploy-5db497dbfb-tvsf5 / Running 16s
测试
[root@k8s-master01 manifests]# kubectl autoscale deployment myapp-deploy --min= --max= --cpu-percent=
deployment.apps "myapp-deploy" autoscaled
[root@k8s-master01 manifests]# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
myapp-deploy Deployment/myapp-deploy <unknown>/% 22s
[root@k8s-master01 pod-dir]# yum install http-tools -y
[root@k8s-master01 pod-dir]# ab -c -n http://172.16.150.213:32222/index.html
[root@k8s-master01 ~]# kubectl describe hpa
Name: myapp-deploy
Namespace: default
Labels: <none>
Annotations: <none>
CreationTimestamp: Sun, Dec :: +
Reference: Deployment/myapp-deploy
Metrics: ( current / target )
resource cpu on pods (as a percentage of request): % (178m) / %
Min replicas:
Max replicas:
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale False BackoffBoth the time since the previous scale is still within both the downscale and upscale forbidden windows
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
ScalingLimited True ScaleUpLimit the desired replica count is increasing faster than the maximum scale rate
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 19m horizontal-pod-autoscaler New size: ; reason: All metrics below target
Normal SuccessfulRescale 2m horizontal-pod-autoscaler New size: ; reason: cpu resource utilization (percentage of request) above target
[root@k8s-master01 ~]# kubectl get pod
NAME READY STATUS RESTARTS AGE
myapp-deploy-5db497dbfb-6kssf / Running 2m
myapp-deploy-5db497dbfb-h7zcb / Running 24m
[root@k8s-master01 ~]# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
myapp-deploy Deployment/myapp-deploy %/% 20m
2、使用v2beat1
[root@k8s-master01 pod-dir]# cat hpa-demo.yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: myapp-hpa-v2
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp-deploy
minReplicas:
maxReplicas:
metrics:
- type: Resource
resource:
name: cpu
targetAverageUtilization:
- type: Resource
resource:
name: memory
targetAverageValue: 100Mi
[root@k8s-master01 pod-dir]# kubectl delete hpa myapp-deploy
horizontalpodautoscaler.autoscaling "myapp-deploy" deleted
[root@k8s-master01 pod-dir]# kubectl apply -f hpa-demo.yaml
horizontalpodautoscaler.autoscaling "myapp-hpa-v2" created
[root@k8s-master01 pod-dir]# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
myapp-hpa-v2 Deployment/myapp-deploy <unknown>/100Mi, <unknown>/% 6s
测试
[root@k8s-master01 ~]# kubectl describe hpa
Name: myapp-hpa-v2
Namespace: default
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...
CreationTimestamp: Sun, Dec :: +
Reference: Deployment/myapp-deploy
Metrics: ( current / target )
resource memory on pods: / 100Mi
resource cpu on pods (as a percentage of request): % (200m) / %
Min replicas:
Max replicas:
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True SucceededRescale the HPA controller was able to update the target scale to
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
ScalingLimited False DesiredWithinRange the desired count is within the acceptable range
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 18s horizontal-pod-autoscaler New size: ; reason: cpu resource utilization (percentage of request) above target
[root@k8s-master01 ~]# kubectl get pod
NAME READY STATUS RESTARTS AGE
myapp-deploy-5db497dbfb-5n885 / Running 26s
myapp-deploy-5db497dbfb-h7zcb / Running 40m
myapp-deploy-5db497dbfb-z2tqd / Running 26s
myapp-deploy-5db497dbfb-zkjhw / Running 26s
[root@k8s-master01 ~]# kubectl describe hpa
Name: myapp-hpa-v2
Namespace: default
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...
CreationTimestamp: Sun, Dec :: +
Reference: Deployment/myapp-deploy
Metrics: ( current / target )
resource memory on pods: / 100Mi
resource cpu on pods (as a percentage of request): % () / %
Min replicas:
Max replicas:
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale False BackoffBoth the time since the previous scale is still within both the downscale and upscale forbidden windows
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from memory resource
ScalingLimited False DesiredWithinRange the desired count is within the acceptable range
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 6m horizontal-pod-autoscaler New size: ; reason: cpu resource utilization (percentage of request) above target
Normal SuccessfulRescale 34s horizontal-pod-autoscaler New size: ; reason: All metrics below target
[root@k8s-master01 ~]# kubectl get pod
NAME READY STATUS RESTARTS AGE
myapp-deploy-5db497dbfb-h7zcb / Running 46m
3.使用v2beat1测试自定义选项
[root@k8s-master01 pod-dir]# cat ../deploy-demon-metrics.yaml
apiVersion: v1
kind: Service
metadata:
name: myapp
namespace: default
spec:
selector:
app: myapp
type: NodePort
ports:
- name: http
port:
targetPort:
nodePort: ---
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-deploy
spec:
replicas:
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: ikubernetes/metrics-app #测试镜像
ports:
- name: httpd
containerPort:
[root@k8s-master01 pod-dir]# kubectl apply -f deploy-demon-metrics.yaml
[root@k8s-master01 pod-dir]# cat hpa-custom.yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: myapp-hpa-v2
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp-deploy
minReplicas:
maxReplicas:
metrics:
- type: Pods #注意类型
pods:
metricName: http_requests #容器中自定义的参数
targetAverageValue: 800m #m表示个数，即800个并发数
[root@k8s-master01 pod-dir]# kubectl apply -f hpa-custom.yaml
[root@k8s-master01 pod-dir]# kubectl describe hpa myapp-hpa-v2
Name: myapp-hpa-v2
Namespace: default
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","ks":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...
CreationTimestamp: Sun, Dec :: +
Reference: Deployment/myapp-deploy
Metrics: ( current / target )
"http_requests" on pods: <unknown> / 800m
Min replicas:
Max replicas:
Events: <none>
[root@k8s-master01 pod-dir]# kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
myapp-hpa-v2 Deployment/myapp-deploy <unknown>/800m 5m
测试：
#好像镜像有点问题，待解决
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