Kubernetes的资源指标API及自定义指标API是什么
发表于：2025-02-02 作者：千家信息网编辑
千家信息网最后更新 2025年02月02日，今天就跟大家聊聊有关Kubernetes的资源指标API及自定义指标API是什么，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。以前是用heap
千家信息网最后更新 2025年02月02日Kubernetes的资源指标API及自定义指标API是什么
今天就跟大家聊聊有关Kubernetes的资源指标API及自定义指标API是什么，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。
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
复制代码
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，如果可以上外网，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 metricsmetrics.k8s.io/v1beta1[root@k8s-node01 ~]# kubectl proxy --port=8080 #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 nodeNAME CPU(cores) CPU% MEMORY(bytes) MEMORY% k8s-master01 176m 4% 3064Mi 39% k8s-node01 62m 1% 4178Mi 54% k8s-node02 65m 1% 2141Mi 27% [root@k8s-node01 ~]# kubectl top podsNAME 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.yaml2.[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.yamlapiVersion: rbac.authorization.k8s.io/v1kind: ClusterRolemetadata: name: system:metrics-serverrules:- apiGroups: - "" resources: - pods - nodes - nodes/stats #添加这一行 - namespaces3.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.yamlcommand: #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=InternalIPcommand: #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storageClassNameapiVersion: v1kind: PersistentVolumemetadata: name: alertmanager spec: capacity: storage: 5Gi accessModes: - ReadWriteOnce - ReadWriteMany persistentVolumeReclaimPolicy: Recycle nfs: path: /data/volumes/v1 server: 172.16.150.158---apiVersion: v1kind: PersistentVolumemetadata: name: standardspec: 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，不能上外网的也可以下载，所以不需要提前下载[root@k8s-node01 prometheus]# kubectl apply -f node-exporter/daemonset.extensions "node-exporter" createdservice "node-exporter" created[root@k8s-node01 prometheus]# kubectl get pod -n k8s-monitor NAME READY STATUS RESTARTS AGEnode-exporter-l5zdw 1/1 Running 0 1mnode-exporter-vwknx 1/1 Running 0 1m
3.安装prometheus
[root@k8s-master_01 prometheus]# kubectl apply -f pv.yaml persistentvolume "alertmanager" configuredpersistentvolume "standard" created[root@k8s-master_01 prometheus]# kubectl get pvNAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGEalertmanager 5Gi RWO,RWX Recycle Available 9sstandard 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 AGEnode-exporter-l5zdw 1/1 Running 0 24mnode-exporter-vwknx 1/1 Running 0 24mprometheus-0 2/2 Running 0 1m[root@k8s-node01 prometheus]# kubectl get svc -n k8s-monitor NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEnode-exporter ClusterIP None9100/TCP 25mprometheus NodePort 10.96.9.121 9090:30090/TCP 22m[root@k8s-master_01 prometheus]# kubectl get pvNAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGEalertmanager 5Gi RWO,RWX Recycle Available 1hstandard 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.yamldeployment.extensions "kube-state-metrics" configured[root@k8s-node01 kube-state-metrics]# kubectl get pod -n k8s-monitor NAME READY STATUS RESTARTS AGEkube-state-metrics-54849b96b4-dmqtk 2/2 Running 0 23snode-exporter-l5zdw 1/1 Running 0 2hnode-exporter-vwknx 1/1 Running 0 2hprometheus-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-monitorkubectl 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 autoscalingautoscaling/v1autoscaling/v2beta1[root@k8s-master01 manifests]# cat deploy-demon.yamlapiVersion: v1kind: Servicemetadata: name: myapp namespace: defaultspec: selector: app: myapp type: NodePort ports: - name: http port: 80 targetPort: 80 nodePort: 32222---apiVersion: apps/v1kind: Deploymentmetadata: name: myapp-deployspec: replicas: 2 selector: matchLabels: app: myapp template: metadata: labels: app: myapp spec: containers: - name: myapp image: ikubernetes/myapp:v2 ports: - name: httpd containerPort: 80 resources: requests: memory: "64Mi" cpu: "100m" limits: memory: "128Mi" cpu: "200m"[root@k8s-master01 manifests]# kubectl get svcNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEkubernetes ClusterIP 10.96.0.1443/TCP 47dmy-nginx NodePort 10.104.13.148 80:32008/TCP 19dmyapp NodePort 10.100.76.180 80:32222/TCP 16stomcat ClusterIP 10.106.222.72 8080/TCP,8009/TCP 19d[root@k8s-master01 manifests]# kubectl get podNAME READY STATUS RESTARTS AGEmyapp-deploy-5db497dbfb-h7zcb 1/1 Running 0 16smyapp-deploy-5db497dbfb-tvsf5 1/1 Running 0 16s
测试
[root@k8s-master01 manifests]# kubectl autoscale deployment myapp-deploy --min=1 --max=8 --cpu-percent=60deployment.apps "myapp-deploy" autoscaled[root@k8s-master01 manifests]# kubectl get hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp-deploy Deployment/myapp-deploy/60% 1 8 0 22s[root@k8s-master01 pod-dir]# yum install http-tools -y[root@k8s-master01 pod-dir]# ab -c 1000 -n 5000000 http://172.16.150.213:32222/index.html[root@k8s-master01 ~]# kubectl describe hpa Name: myapp-deployNamespace: defaultLabels: Annotations: CreationTimestamp: Sun, 16 Dec 2018 20:34:41 +0800Reference: Deployment/myapp-deployMetrics: ( current / target ) resource cpu on pods (as a percentage of request): 178% (178m) / 60%Min replicas: 1Max replicas: 8Conditions: 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 rateEvents: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulRescale 19m horizontal-pod-autoscaler New size: 1; reason: All metrics below target Normal SuccessfulRescale 2m horizontal-pod-autoscaler New size: 2; reason: cpu resource utilization (percentage of request) above target[root@k8s-master01 ~]# kubectl get podNAME READY STATUS RESTARTS AGEmyapp-deploy-5db497dbfb-6kssf 1/1 Running 0 2mmyapp-deploy-5db497dbfb-h7zcb 1/1 Running 0 24m[root@k8s-master01 ~]# kubectl get hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp-deploy Deployment/myapp-deploy 178%/60% 1 8 2 20m
2、使用v2beat1
[root@k8s-master01 pod-dir]# cat hpa-demo.yaml apiVersion: autoscaling/v2beta1kind: HorizontalPodAutoscalermetadata: name: myapp-hpa-v2spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: myapp-deploy minReplicas: 1 maxReplicas: 10 metrics: - type: Resource resource: name: cpu targetAverageUtilization: 55 - 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 hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp-hpa-v2 Deployment/myapp-deploy/100Mi, /55% 1 10 0 6s
测试
[root@k8s-master01 ~]# kubectl describe hpa Name: myapp-hpa-v2Namespace: defaultLabels:Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...CreationTimestamp: Sun, 16 Dec 2018 21:07:25 +0800Reference: Deployment/myapp-deployMetrics: ( current / target ) resource memory on pods: 1765376 / 100Mi resource cpu on pods (as a percentage of request): 200% (200m) / 55%Min replicas: 1Max replicas: 10Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True SucceededRescale the HPA controller was able to update the target scale to 4 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 rangeEvents: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulRescale 18s horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target[root@k8s-master01 ~]# kubectl get podNAME READY STATUS RESTARTS AGEmyapp-deploy-5db497dbfb-5n885 1/1 Running 0 26smyapp-deploy-5db497dbfb-h7zcb 1/1 Running 0 40mmyapp-deploy-5db497dbfb-z2tqd 1/1 Running 0 26smyapp-deploy-5db497dbfb-zkjhw 1/1 Running 0 26s[root@k8s-master01 ~]# kubectl describe hpa Name: myapp-hpa-v2Namespace: defaultLabels: Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...CreationTimestamp: Sun, 16 Dec 2018 21:07:25 +0800Reference: Deployment/myapp-deployMetrics: ( current / target ) resource memory on pods: 1765376 / 100Mi resource cpu on pods (as a percentage of request): 0% (0) / 55%Min replicas: 1Max replicas: 10Conditions: 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 rangeEvents: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulRescale 6m horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target Normal SuccessfulRescale 34s horizontal-pod-autoscaler New size: 1; reason: All metrics below target[root@k8s-master01 ~]# kubectl get podNAME READY STATUS RESTARTS AGEmyapp-deploy-5db497dbfb-h7zcb 1/1 Running 0 46m
3.使用v2beat1测试自定义选项
[root@k8s-master01 pod-dir]# cat ../deploy-demon-metrics.yamlapiVersion: v1kind: Servicemetadata: name: myapp namespace: defaultspec: selector: app: myapp type: NodePort ports: - name: http port: 80 targetPort: 80 nodePort: 32222---apiVersion: apps/v1kind: Deploymentmetadata: name: myapp-deployspec: replicas: 2 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: 80[root@k8s-master01 pod-dir]# kubectl apply -f deploy-demon-metrics.yaml[root@k8s-master01 pod-dir]# cat hpa-custom.yaml apiVersion: autoscaling/v2beta1kind: HorizontalPodAutoscalermetadata: name: myapp-hpa-v2spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: myapp-deploy minReplicas: 1 maxReplicas: 10 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-v2Namespace: defaultLabels:Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"autoscaling/v2beta1","ks":{},"name":"myapp-hpa-v2","namespace":"default"},"spec":{...CreationTimestamp: Sun, 16 Dec 2018 22:09:32 +0800Reference: Deployment/myapp-deployMetrics: ( current / target ) "http_requests" on pods: / 800mMin replicas: 1Max replicas: 10Events: [root@k8s-master01 pod-dir]# kubectl get hpaNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGEmyapp-hpa-v2 Deployment/myapp-deploy /800m 1 10 2 5m
看完上述内容，你们对Kubernetes的资源指标API及自定义指标API是什么有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注行业资讯频道，感谢大家的支持。
指标
数据
镜像
文件
端口
测试
资源
版本
选择
模板
参数
名称
地址
内容
项目
搜索
数据源
方式
核心
类型
数据库的安全要保护哪些东西
数据库安全各自的含义是什么
生产安全数据库录入
数据库的安全性及管理
数据库安全策略包含哪些
海淀数据库安全审计系统
建立农村房屋安全信息数据库
易用的数据库客户端支持安全管理
连接数据库失败ssl安全错误
数据库的锁怎样保障安全
gta5服务器维护
全光网络技术联盟官网
数据库和图形学
数据库在备份是可以操作吗
数据库 数字转换成文本格式
成都软件开发解决方案咨
达梦数据库免费版下载安装
精雕机加数据库
pg数据库垂直分表
三大经典的数据库模型是什么
没有LPROMIS数据库连接
数据库列级指定外键
华为服务器上海代理商名单
河北安卓软件开发哪家正规
小程序需要每年租服务器吗
网络安全知识测试总结
互联网掌阅科技
32位连接64位数据库吗
上海爱伯互联网科技公司
软软件开发与软件维护
大旗网络安全海报
河北网络技术转让是什么
2005sql数据库复制
宿州映雪网络技术服务有限公司
做软件开发好找工作吗
考研软件开发专业大学排名
河北置家网络技术邯郸
恋恋视频软件开发
徐汇区数据软件开发有哪些
网络技术对饭圈文化的影响
