Kubernetes之（十九）资源指标和集群监控

时间 2019-11-30

标签 kubernetes 十九资源指标集群监控栏目负载均衡繁體版

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目录node

Kubernetes之（十九）资源指标和集群监控

Kubernetes之（十九）资源指标和集群监控

资源指标和资源监控

一个集群系统管理离不开监控，一样的Kubernetes也须要根据数据指标来采集相关数据，从而完成对集群系统的监控情况进行监测。这些指标整体上分为两个组成：监控集群自己和监控Pod对象，一般一个集群的衡量性指标包括如下几个部分：mysql

节点资源状态：主要包括网络带宽、磁盘空间、CPU和内存使用率
节点的数量：即时性了解集群的可用节点数量能够为用户计算服务器使用的费用支出提供参考。
运行的Pod对象：正在运行的Pod对象数量能够评估可用节点数量是否足够，以及节点故障时是否能平衡负载。

另外一个方面，对Pod资源对象的监控需求大概有如下三类：linux

Kubernetes指标：监测特定应用程序相关的Pod对象的部署过程、副本数量、状态信息、健康状态、网络等等。
容器指标：容器的资源需求、资源限制、CPU、内存、磁盘空间、网络带宽的实际占用状况。
应用程序指标：应用程序自身的内建指标，和业务规则相关

metrics-server

在新一代的Kubernetes指标监控体系当中主要由核心指标流水线和监控指标流水线组成：git

核心指标流水线：是指由kubelet、、metrics-server以及由API server提供的api组成，它们能够为K8S系统提供核心指标，从而了解并操做集群内部组件和程序。其中相关的指标包括CPU的累积使用率、内存实时使用率，Pod资源占用率以及容器磁盘占用率等等。其中核心指标的获取原先是由heapster进行收集，可是在1.11版本以后已经被废弃，从而由新一代的metrics-server所代替对核心指标的汇聚。核心指标的收集是必要的。以下图：
github
监控指标流水线：用于从系统收集各类指标数据并提供给终端用户、存储系统以及HPA。它们包含核心指标以及许多非核心指标，其中因为非核心指标自己不能被Kubernetes所解析，此时就须要依赖于用户选择第三方解决方案。以下图：web

一个能够同时使用资源指标API和自定义指标API的组件是HPAv2，其实现了经过观察指标实现自动扩容和缩容。而目前资源指标API的实现主流是metrics-server。sql

自1.8版本后，容器的cpu和内存资源占用利用率均可以经过客户端指标API直接调用，从而获取资源使用状况，要知道的是API自己并不存储任何指标数据，仅仅提供资源占用率的实时监测数据。数据库

资源指标和其余的API指标并无啥区别，它是经过API Server的URL路径/apis/metrics.k8s.io/进行存取，只有在k8s集群内部署了metrics-server应用才能只用API，其简单的结构图以下：

MetricsServer基于内存存储，重启后数据将所有丢失，并且它仅能留存最近收集到的指标数据，所以，若是用户指望访问历史数据，就不得不借助于第三方的监控系统（如Prometheus等）。vim

通常说来，MetricsServer在每一个集群中仅会运行一个实例，启动时，它将自动初始化与各节点的链接，所以出于安全方面的考虑，它须要运行于普通节点而非Master主机之上。直接使用项目自己提供的资源配置清单即能轻松完成metrics-server的部署。后端

部署metrics-server

https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/metrics-server
下载yaml文件

[root@master metrics-server]# for n 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/master/cluster/addons/metrics-server/$n;done

[root@master metrics-server]# ll
总用量 24
-rw-r--r-- 1 root root  398 4月  10 10:31 auth-delegator.yaml
-rw-r--r-- 1 root root  419 4月  10 10:31 auth-reader.yaml
-rw-r--r-- 1 root root  393 4月  10 10:32 metrics-apiservice.yaml
-rw-r--r-- 1 root root 3156 4月  10 10:32 metrics-server-deployment.yaml
-rw-r--r-- 1 root root  336 4月  10 10:32 metrics-server-service.yaml
-rw-r--r-- 1 root root  801 4月  10 10:32 resource-reader.yaml

部署

#因为镜像及部分设置问题，修改下面这个文件的部份内容
#metrics-server容器修改镜像地址和command字段，metrics-server-nanny容器中的cpu和内存值
[root@master metrics-server]# vim metrics-server-deployment.yaml 
......
    spec:
      priorityClassName: system-cluster-critical
      serviceAccountName: metrics-server
      containers:
      - name: metrics-server
        #image: k8s.gcr.io/metrics-server-amd64:v0.3.1
        image: xiaobai20201/metrics-server:v0.3.1
      - name: metrics-server
        #image: k8s.gcr.io/metrics-server-amd64:v0.3.1
        image: xiaobai20201/metrics-server:v0.3.1
        command:
        - /metrics-server
        - --metric-resolution=30s
        - --kubelet-insecure-tls
        - --kubelet-preferred-address-types=InternalIP,Hostname,InternalDNS,ExternalDNS,ExternalIP
        ports:
        - containerPort: 443
          name: https
          protocol: TCP
      - name: metrics-server-nanny
        #image: k8s.gcr.io/addon-resizer:1.8.4
        image: xiaobai20201/addon-resizer:1.8.4
        resources:
          limits:
            cpu: 100m
            memory: 300Mi
          requests:
            cpu: 5m
          # Specifies the smallest cluster (defined in number of nodes)
          # resources will be scaled to.
            memory: 50Mi
        env:
          - name: MY_POD_NAME
            valueFrom:
              fieldRef:
                fieldPath: metadata.name
          - name: MY_POD_NAMESPACE
            valueFrom:
              fieldRef:
                fieldPath: metadata.namespace
        volumeMounts:
        - name: metrics-server-config-volume
          mountPath: /etc/config
        command:
          - /pod_nanny
          - --config-dir=/etc/config
          - --cpu=100m
          - --extra-cpu=0.5m
          - --memory=100Mi
          - --extra-memory=50Mi
          - --threshold=5
          - --deployment=metrics-server-v0.3.1
          - --container=metrics-server
          - --poll-period=300000
          - --estimator=exponential
          - --minClusterSize=10
          
[root@master metrics-server]# vim resource-reader.yaml 
#因为启动容器还须要权限获取数据，须要在resource-reader.yaml文件中增长nodes/stats

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: system:metrics-server
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups:
  - ""
  resources:
  - pods
  - nodes
  - nodes/stats
  - namespaces
  verbs:
  - get
  - list
  - watch

部署

[root@master metrics-server]# kubectl apply -f .
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
configmap/metrics-server-config created
deployment.apps/metrics-server-v0.3.1 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 metrics-server]# kubectl api-versions |grep metrics
metrics.k8s.io/v1beta1

#检查资源指标API的可用性
[root@master metrics-server]# kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes"
{"kind":"NodeMetricsList","apiVersion":"metrics.k8s.io/v1beta1","metadata":{"selfLink":"/apis/metrics.k8s.io/v1beta1/nodes"},"items":[]}

#部署成功后可使用kubectl proxy --port=8080来代理出一个端口
[root@master metrics-server]# kubectl proxy --port=8080
Starting to serve on 127.0.0.1:8080
#使用curl命令能够从api接口查看节点等状态
[root@master mainfest]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1
{
  "kind": "APIResourceList",
  "apiVersion": "v1",
  "groupVersion": "metrics.k8s.io/v1beta1",
  "resources": [
    {
      "name": "nodes",
      "singularName": "",
      "namespaced": false,
      "kind": "NodeMetrics",
      "verbs": [
        "get",
        "list"
      ]
    },
    {
      "name": "pods",
      "singularName": "",
      "namespaced": true,
      "kind": "PodMetrics",
      "verbs": [
        "get",
        "list"
      ]
    }
  ]
}

#该组内主要提供nodes和pods的数据

[root@master mainfest]# curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes
{
  "kind": "NodeMetricsList",
  "apiVersion": "metrics.k8s.io/v1beta1",
  "metadata": {
    "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes"
  },
  "items": [
    {
      "metadata": {
        "name": "node02",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node02",
        "creationTimestamp": "2019-04-10T02:57:21Z"
      },
      "timestamp": "2019-04-10T02:57:14Z",
      "window": "30s",
      "usage": {
        "cpu": "41332743n",
        "memory": "702124Ki"
      }
    },
    {
      "metadata": {
        "name": "master",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/master",
        "creationTimestamp": "2019-04-10T02:57:21Z"
      },
      "timestamp": "2019-04-10T02:57:15Z",
      "window": "30s",
      "usage": {
        "cpu": "156316878n",
        "memory": "1209616Ki"
      }
    },
    {
      "metadata": {
        "name": "node01",
        "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/node01",
        "creationTimestamp": "2019-04-10T02:57:21Z"
      },
      "timestamp": "2019-04-10T02:57:09Z",
      "window": "30s",
      "usage": {
        "cpu": "47843790n",
        "memory": "800144Ki"
      }
    }
  ]
}

下面使用kubectl top命令进行查看资源信息：

[root@master metrics-server]# kubectl top nodes
NAME     CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%   
master   146m         7%     1187Mi          68%       
node01   45m          4%     782Mi           45%       
node02   36m          3%     683Mi           39%   



[root@master mainfest]# kubectl top pods -n kube-system
NAME                                     CPU(cores)   MEMORY(bytes)   
canal-nbspn                              21m          52Mi            
canal-pj6rx                              13m          43Mi            
canal-rgsnp                              12m          43Mi            
coredns-78d4cf999f-6cb69                 2m           10Mi            
coredns-78d4cf999f-tflpn                 2m           10Mi            
etcd-master                              16m          121Mi           
kube-apiserver-master                    31m          517Mi           
kube-controller-manager-master           39m          82Mi            
kube-flannel-ds-amd64-5zrk7              2m           14Mi            
kube-flannel-ds-amd64-pql5n              2m           12Mi            
kube-flannel-ds-amd64-ssd29              2m           14Mi            
kube-proxy-ch4vp                         2m           15Mi            
kube-proxy-cz2rf                         2m           23Mi            
kube-proxy-kdp7d                         4m           21Mi            
kube-scheduler-master                    10m          21Mi            
kubernetes-dashboard-6f9998798-klf4t     1m           15Mi            
metrics-server-v0.3.1-65bd5d59b9-xvmns   1m           20Mi 



[root@master metrics-server]# kubectl top pod -l k8s-app=kube-dns --containers=true -n kube-system
POD                        NAME      CPU(cores)   MEMORY(bytes)   
coredns-78d4cf999f-6cb69   coredns   2m           10Mi            
coredns-78d4cf999f-tflpn   coredns   2m           10Mi

Prometheus

概述

除了前面的资源指标（如CPU、内存）之外，用户或管理员须要了解更多的指标数据，好比Kubernetes指标、容器指标、节点资源指标以及应用程序指标等等。自定义指标API容许请求任意的指标，其指标API的实现要指定相应的后端监视系统。而Prometheus是第一个开发了相应适配器的监控系统。这个适用于Prometheus的Kubernetes Customm Metrics Adapter是属于Github上的k8s-prometheus-adapter项目提供的。其原理图以下：

prometheus自己就是一监控系统，也分为server端和agent端，server端从被监控主机获取数据，而agent端须要部署一个node_exporter，主要用于数据采集和暴露节点的数据，那么在获取Pod级别或者是mysql等多种应用的数据，也是须要部署相关的exporter。咱们能够经过PromQL的方式对数据进行查询，可是因为自己prometheus属于第三方的解决方案，原生的k8s系统并不能对Prometheus的自定义指标进行解析，就须要借助于k8s-prometheus-adapter将这些指标数据查询接口转换为标准的Kubernetes自定义指标。

Prometheus是一个开源的服务监控系统和时序数据库，其提供了通用的数据模型和快捷数据采集、存储和查询接口。它的核心组件Prometheus服务器按期从静态配置的监控目标或者基于服务发现自动配置的目标中进行拉取数据，新拉取到啊的数据大于配置的内存缓存区时，数据就会持久化到存储设备当中。Prometheus组件架构图以下：

每一个被监控的主机均可以经过专用的exporter程序提供输出监控数据的接口，并等待Prometheus服务器周期性的进行数据抓取。若是存在告警规则，则抓取到数据以后会根据规则进行计算，知足告警条件则会生成告警，并发送到Alertmanager完成告警的汇总和分发。当被监控的目标有主动推送数据的需求时，能够以Pushgateway组件进行接收并临时存储数据，而后等待Prometheus服务器完成数据的采集。

任何被监控的目标都须要事先归入到监控系统中才能进行时序数据采集、存储、告警和展现，监控目标能够经过配置信息以静态形式指定，也可让Prometheus经过服务发现的机制进行动态管理。下面是组件的一些解析：

监控代理程序：如node_exporter：收集主机的指标数据，如平均负载、CPU、内存、磁盘、网络等等多个维度的指标数据。
kubelet（cAdvisor）：收集容器指标数据，也是K8S的核心指标收集，每一个容器的相关指标数据包括：CPU使用率、限额、文件系统读写限额、内存使用率和限额、网络报文发送、接收、丢弃速率等等。
API Server：收集API Server的性能指标数据，包括控制队列的性能、请求速率和延迟时长等等
etcd：收集etcd存储集群的相关指标数据
kube-state-metrics：该组件能够派生出k8s相关的多个指标数据，主要是资源类型相关的计数器和元数据信息，包括制定类型的对象总数、资源限额、容器状态以及Pod资源标签系列等。

Prometheus可以直接把KubernetesAPIServer做为服务发现系统使用进而动态发现和监控集群中的全部可被监控的对象。这里须要特别说明的是，Pod资源须要添加下列注解信息才能被Prometheus系统自动发现并抓取其内建的指标数据。

prometheus.io/scrape：用于标识是否须要被采集指标数据，布尔型值，true或false。
prometheus.io/path：抓取指标数据时使用的URL路径，通常为/metrics。
prometheus.io/port ：抓取指标数据时使用的套接字端口，如8080。

另外，仅指望Prometheus为后端生成自定义指标时仅部署Prometheus服务器便可，它甚至也不须要数据持久功能。但若要配置完整功能的监控系统，管理员还须要在每一个主机上部署node_exporter、按需部署其余特有类型的exporter以及Alertmanager。

部署prometheus

官方地址：https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/prometheus
因为官方的YAML部署方式须要使用到PVC，这里使用马哥提供的学习类型的部署，具体生产仍是须要根据官方的建议进行。

[root@master metrics]# git clone https://github.com/iKubernetes/k8s-prom.git 
正克隆到 'k8s-prom'...
remote: Enumerating objects: 49, done.
remote: Total 49 (delta 0), reused 0 (delta 0), pack-reused 49
Unpacking objects: 100% (49/49), done.

建立名称空间prom

[root@master metrics]# cd k8s-prom/
[root@master k8s-prom]# ls
k8s-prometheus-adapter  kube-state-metrics  namespace.yaml  node_exporter  podinfo  prometheus  README.md
[root@master k8s-prom]# kubectl apply -f namespace.yaml
namespace/prom created

部署node_exporter

[root@master k8s-prom]# cd node_exporter/
[root@master node_exporter]# kubectl apply -f .
daemonset.apps/prometheus-node-exporter created
service/prometheus-node-exporter created

[root@master node_exporter]# kubectl get ds -n prom
NAME                       DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR   AGE
prometheus-node-exporter   3         3         3       3            3           <none>          100s
[root@master node_exporter]# kubectl get pods -n prom  
NAME                             READY   STATUS    RESTARTS   AGE
prometheus-node-exporter-b2lk5   1/1     Running   0          104s
prometheus-node-exporter-d4l6v   1/1     Running   0          104s
prometheus-node-exporter-swngp   1/1     Running   0          104s

部署prometheus-server

[root@master node_exporter]# cd ../prometheus/
[root@master prometheus]# ll
总用量 24
-rw-r--r-- 1 root root 10132 4月  10 11:20 prometheus-cfg.yaml
-rw-r--r-- 1 root root  1481 4月  10 11:20 prometheus-deploy.yaml
-rw-r--r-- 1 root root   716 4月  10 11:20 prometheus-rbac.yaml
-rw-r--r-- 1 root root   278 4月  10 11:20 prometheus-svc.yaml
[root@master prometheus]# kubectl apply -f .
configmap/prometheus-config created
deployment.apps/prometheus-server created
clusterrole.rbac.authorization.k8s.io/prometheus created
serviceaccount/prometheus created
clusterrolebinding.rbac.authorization.k8s.io/prometheus created
service/prometheus created
#因为prometheus的yaml内内存limit为2G，此时node节点虚拟机均不知足要求，致使会一直是pending状态，此处进行修改，
[root@master prometheus]# vim prometheus-deploy.yaml
        #resources:
         # limits:
          #  memory: 2Gi
[root@master prometheus]# kubectl apply -f prometheus-deploy.yaml
deployment.apps/prometheus-server configured

[root@master prometheus]# kubectl get pods -n prom -w
NAME                                 READY   STATUS    RESTARTS   AGE
prometheus-node-exporter-b2lk5       1/1     Running   0          9m30s
prometheus-node-exporter-d4l6v       1/1     Running   0          9m30s
prometheus-node-exporter-swngp       1/1     Running   0          9m30s
prometheus-server-556b8896d6-ld7xj   1/1     Running   0          35s

部署后查看日志

[root@master prometheus]# kubectl logs prometheus-server-556b8896d6-ld7xj -n prom
level=info ts=2019-04-10T03:33:57.752158604Z caller=main.go:220 msg="Starting Prometheus" version="(version=2.2.1, branch=HEAD, revision=bc6058c81272a8d938c05e75607371284236aadc)"
level=info ts=2019-04-10T03:33:57.752221598Z caller=main.go:221 build_context="(go=go1.10, user=root@149e5b3f0829, date=20180314-14:15:45)"
level=info ts=2019-04-10T03:33:57.752240032Z caller=main.go:222 host_details="(Linux 3.10.0-862.el7.x86_64 #1 SMP Fri Apr 20 16:44:24 UTC 2018 x86_64 prometheus-server-556b8896d6-ld7xj (none))"
level=info ts=2019-04-10T03:33:57.752255713Z caller=main.go:223 fd_limits="(soft=65536, hard=65536)"
level=info ts=2019-04-10T03:33:57.755420653Z caller=main.go:504 msg="Starting TSDB ..."
level=info ts=2019-04-10T03:33:57.7620657Z caller=web.go:382 component=web msg="Start listening for connections" address=0.0.0.0:9090
level=info ts=2019-04-10T03:33:57.7632425Z caller=main.go:514 msg="TSDB started"
level=info ts=2019-04-10T03:33:57.764611774Z caller=main.go:588 msg="Loading configuration file" filename=/etc/prometheus/prometheus.yml
level=info ts=2019-04-10T03:33:57.765669001Z caller=kubernetes.go:191 component="discovery manager scrape" discovery=k8s msg="Using pod service account via in-cluster config"
level=info ts=2019-04-10T03:33:57.76626263Z caller=kubernetes.go:191 component="discovery manager scrape" discovery=k8s msg="Using pod service account via in-cluster config"
level=info ts=2019-04-10T03:33:57.76668914Z caller=kubernetes.go:191 component="discovery manager scrape" discovery=k8s msg="Using pod service account via in-cluster config"
level=info ts=2019-04-10T03:33:57.767331363Z caller=kubernetes.go:191 component="discovery manager scrape" discovery=k8s msg="Using pod service account via in-cluster config"
level=info ts=2019-04-10T03:33:57.768433541Z caller=kubernetes.go:191 component="discovery manager scrape" discovery=k8s msg="Using pod service account via in-cluster config"
level=info ts=2019-04-10T03:33:57.768948262Z caller=main.go:491 msg="Server is ready to receive web requests."

此时可使用NodeIP:30090 进行访问,并能够查看监控，内部已经内置了了一些监控指标

部署kube-state-metrics

[root@master prometheus]# cd ../kube-state-metrics/
#修改 kube-state-metrics-deploy.yaml内的image地址
        image: xiaobai20201/kube-state-metrics-amd64:v1.3.1

[root@master kube-state-metrics]# kubectl apply -f .
deployment.apps/kube-state-metrics created
serviceaccount/kube-state-metrics created
clusterrole.rbac.authorization.k8s.io/kube-state-metrics created
clusterrolebinding.rbac.authorization.k8s.io/kube-state-metrics created
service/kube-state-metrics created

[root@master kube-state-metrics]# kubectl get pods -n prom          
NAME                                 READY   STATUS    RESTARTS   AGE
kube-state-metrics-84c69bb8-87l7n    1/1     Running   0          19s
prometheus-node-exporter-b2lk5       1/1     Running   0          21m
prometheus-node-exporter-d4l6v       1/1     Running   0          21m
prometheus-node-exporter-swngp       1/1     Running   0          21m
prometheus-server-556b8896d6-ld7xj   1/1     Running   0          12m

制做证书
因为默认状况下K8S集群都是基于https提供服务，而默认状况k8s-prometheus-adapter是基于http服务，须要提供该K8S服务器CA签署承认的证书，因此须要自制证书

[root@master kube-state-metrics]# cd /etc/kubernetes/pki/
[root@master pki]# (umask 077;openssl genrsa -out serving.key)
Generating RSA private key, 2048 bit long modulus
.........+++
..+++
e is 65537 (0x10001)

[root@master pki]# openssl x509 -req -in serving.csr -CA ./ca.crt -CAkey ./ca.key -CAcreateserial -out serving.crt -days 3650
Signature ok
subject=/CN=serving
Getting CA Private Key


[root@master pki]#  kubectl create secret generic cm-adapter-serving-certs --from-file=serving.crt=./serving.crt --from-file=serving.key -n prom
secret/cm-adapter-serving-certs created
[root@master pki]# kubectl get secret -n prom
NAME                             TYPE                                  DATA   AGE
cm-adapter-serving-certs         Opaque                                2      13s
default-token-r88nt              kubernetes.io/service-account-token   3      37m
kube-state-metrics-token-4rrqw   kubernetes.io/service-account-token   3      14m
prometheus-token-jdm5f           kubernetes.io/service-account-token   3      31m

部署k8s-prometheus-adapter
这里自带的custom-metrics-apiserver-deployment.yaml和custom-metrics-config-map.yaml有点问题，须要下载k8s-prometheus-adapter项目中的这2个文件

[root@master k8s-prometheus-adapter]#  wget https://raw.githubusercontent.com/DirectXMan12/k8s-prometheus-adapter/master/deploy/manifests/custom-metrics-apiserver-deployment.yaml
[root@master k8s-prometheus-adapter]#  wget https://raw.githubusercontent.com/DirectXMan12/k8s-prometheus-adapter/master/deploy/manifests/custom-metrics-config-map.yaml 
#修改下载文件的内容的namespace为prom

执行

[root@master k8s-prometheus-adapter]# kubectl apply -f .
clusterrolebinding.rbac.authorization.k8s.io/custom-metrics:system:auth-delegator created
rolebinding.rbac.authorization.k8s.io/custom-metrics-auth-reader created
deployment.apps/custom-metrics-apiserver created
clusterrolebinding.rbac.authorization.k8s.io/custom-metrics-resource-reader created
serviceaccount/custom-metrics-apiserver created
service/custom-metrics-apiserver created
apiservice.apiregistration.k8s.io/v1beta1.custom.metrics.k8s.io created
clusterrole.rbac.authorization.k8s.io/custom-metrics-server-resources created
configmap/adapter-config created
clusterrole.rbac.authorization.k8s.io/custom-metrics-resource-reader created
clusterrolebinding.rbac.authorization.k8s.io/hpa-controller-custom-metrics created

[root@master k8s-prometheus-adapter]# kubectl get pods -n prom
NAME                                      READY   STATUS    RESTARTS   AGE
custom-metrics-apiserver-c86bfc77-dtkcn   1/1     Running   0          58s
kube-state-metrics-84c69bb8-87l7n         1/1     Running   0          140m
prometheus-node-exporter-b2lk5            1/1     Running   0          161m
prometheus-node-exporter-d4l6v            1/1     Running   0          161m
prometheus-node-exporter-swngp            1/1     Running   0          161m
prometheus-server-556b8896d6-ld7xj        1/1     Running   0          152m

[root@master k8s-prometheus-adapter]# kubectl api-versions |grep custom
custom.metrics.k8s.io/v1beta1

Grafana数据展现

[root@master metrics]# vim grafana.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: monitoring-grafana
  namespace: prom    #修更名称空间
spec:
  replicas: 1
  selector:
    matchLabels:
      task: monitoring
      k8s-app: grafana
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: grafana
    spec:
      containers:
      - name: grafana
        image: registry.cn-hangzhou.aliyuncs.com/google_containers/heapster-grafana-amd64:v5.0.4
        ports:
        - containerPort: 3000
          protocol: TCP
        volumeMounts:
        - mountPath: /etc/ssl/certs
          name: ca-certificates
          readOnly: true
        - mountPath: /var
          name: grafana-storage
        env:    #这里使用的是原先的heapster的grafana的配置文件，须要注释掉这个环境变量
        #- name: INFLUXDB_HOST
        #        #  value: monitoring-influxdb
        - name: GF_SERVER_HTTP_PORT
          value: "3000"
        - name: GF_AUTH_BASIC_ENABLED
          value: "false"
        - name: GF_AUTH_ANONYMOUS_ENABLED
          value: "true"
        - name: GF_AUTH_ANONYMOUS_ORG_ROLE
          value: Admin
        - name: GF_SERVER_ROOT_URL
          value: /
      volumes:
      - name: ca-certificates
        hostPath:
          path: /etc/ssl/certs
      - name: grafana-storage
        emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
  labels:
    kubernetes.io/cluster-service: 'true'
    kubernetes.io/name: monitoring-grafana
  name: monitoring-grafana
  namespace: prom
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 3000
  selector:
    k8s-app: grafana
    
[root@master metrics]# kubectl apply -f grafana.yaml
deployment.apps/monitoring-grafana created
service/monitoring-grafana created

[root@master metrics]# kubectl get pods -n prom 
NAME                                      READY   STATUS    RESTARTS   AGE
custom-metrics-apiserver-c86bfc77-dtkcn   1/1     Running   0          8m56s
kube-state-metrics-84c69bb8-87l7n         1/1     Running   0          148m
monitoring-grafana-dcf785fd8-f7q4g        1/1     Running   0          2m4s
prometheus-node-exporter-b2lk5            1/1     Running   0          169m
prometheus-node-exporter-d4l6v            1/1     Running   0          169m
prometheus-node-exporter-swngp            1/1     Running   0          169m
prometheus-server-556b8896d6-ld7xj        1/1     Running   0          160m

[root@master metrics]# kubectl get svc -n prom
NAME                       TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)          AGE
custom-metrics-apiserver   ClusterIP   10.107.119.218   <none>        443/TCP          9m16s
kube-state-metrics         ClusterIP   10.103.206.116   <none>        8080/TCP         149m
monitoring-grafana         NodePort    10.109.0.252     <none>        80:30215/TCP     2m23s
prometheus                 NodePort    10.101.97.208    <none>        9090:30090/TCP   166m
prometheus-node-exporter   ClusterIP   None             <none>        9100/TCP         169m

monitoring-grafana暴露端口为30215
使用浏览器访问 http://10.0.0.10:30215

默认是没有kubernetes的模板的，能够到grafana.com中去下载相关的kubernetes模板。
https://grafana.com/dashboards

参考资料

https://www.cnblogs.com/linuxk 马永亮. Kubernetes进阶实战 (云计算与虚拟化技术丛书) Kubernetes-handbook-jimmysong-20181218