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1.1 深度解读Prometheus

1.1.1 什么是Prometheus？

Prometheus是一个开源的系统监控和报警系统，现在已经加入到CNCF基金会，成为继k8s之后第二个在CNCF托管的项目，在kubernetes容器管理系统中，通常会搭配prometheus进行监控，同时也支持多种exporter采集数据，还支持pushgateway进行数据上报，Prometheus性能足够支撑上万台规模的集群。

1.1.2 prometheus特点

1.多维度数据模型

时间序列数据由metrics名称和键值对来组成

可以对数据进行聚合，切割等操作

所有的metrics都可以设置任意的多维标签。

2.灵活的查询语言（PromQL）

可以对采集的metrics指标进行加法，乘法，连接等操作；

3.可以直接在本地部署，不依赖其他分布式存储；

4.通过基于HTTP的pull方式采集时序数据；

5.可以通过中间网关pushgateway的方式把时间序列数据推送到prometheus server端；

6.可通过服务发现或者静态配置来发现目标服务对象（targets）。

7.有多种可视化图像界面，如Grafana等。

8.高效的存储，每个采样数据占3.5 bytes左右，300万的时间序列，30s间隔，保留60天，消耗磁盘大概200G。

9.做高可用，可以对数据做异地备份，联邦集群，部署多套prometheus，pushgateway上报数据

1.1.3 prometheus组件

从上图可发现，Prometheus整个生态圈组成主要包括prometheus server，Exporter，pushgateway，alertmanager，grafana，Web ui界面，Prometheusserver由三个部分组成，Retrieval，Storage，PromQL

1.Retrieval负责在活跃的target主机上抓取监控指标数据

2.Storage存储主要是把采集到的数据存储到磁盘中

3.PromQL是Prometheus提供的查询语言模块。

1.PrometheusServer:

用于收集和存储时间序列数据。

2.ClientLibrary:

客户端库，检测应用程序代码，当Prometheus抓取实例的HTTP端点时，客户端库会将所有跟踪的metrics指标的当前状态发送到prometheus server端。

3.Exporters:

prometheus支持多种exporter，通过exporter可以采集metrics数据，然后发送到prometheus server端，所有向promtheus server提供监控数据的程序都可以被称为exporter

4.Alertmanager:

从 Prometheusserver 端接收到 alerts 后，会进行去重，分组，并路由到相应的接收方，发出报警，常见的接收方式有：电子邮件，微信，钉钉, slack等。

5.Grafana：

监控仪表盘，可视化监控数据

6.pushgateway:

各个目标主机可上报数据到pushgatewy，然后prometheus server统一从pushgateway拉取数据。

1.1.4 prometheus几种部署模式

基本HA模式

基本的HA模式只能确保Promthues服务的可用性问题，但是不解决Prometheus Server之间的数据一致性问题以及持久化问题(数据丢失后无法恢复)，也无法进行动态的扩展。因此这种部署方式适合监控规模不大，Promthues Server也不会频繁发生迁移的情况，并且只需要保存短周期监控数据的场景。

基本HA + 远程存储方案

在解决了Promthues服务可用性的基础上，同时确保了数据的持久化，当Promthues Server发生宕机或者数据丢失的情况下，可以快速的恢复。同时PromthuesServer可能很好的进行迁移。因此，该方案适用于用户监控规模不大，但是希望能够将监控数据持久化，同时能够确保PromthuesServer的可迁移性的场景。

基本HA + 远程存储 + 联邦集群方案

Promthues的性能瓶颈主要在于大量的采集任务，因此用户需要利用Prometheus联邦集群的特性，将不同类型的采集任务划分到不同的Promthues子服务中，从而实现功能分区。例如一个Promthues Server负责采集基础设施相关的监控指标，另外一个Prometheus Server负责采集应用监控指标。再有上层Prometheus Server实现对数据的汇聚。
1.1.5  prometheus工作流程1.  Prometheus  server可定期从活跃的（up）目标主机上（target）拉取监控指标数据，目标主机的监控数据可通过配置静态job或者服务发现的方式被prometheus server采集到，这种方式默认的pull方式拉取指标；也可通过pushgateway把采集的数据上报到prometheus server中；还可通过一些组件自带的exporter采集相应组件的数据；2.Prometheus server把采集到的监控指标数据保存到本地磁盘或者数据库；3.Prometheus采集的监控指标数据按时间序列存储，通过配置报警规则，把触发的报警发送到alertmanager4.Alertmanager通过配置报警接收方，发送报警到邮件，微信或者钉钉等5.Prometheus 自带的web ui界面提供PromQL查询语言，可查询监控数据6.Grafana可接入prometheus数据源，把监控数据以图形化形式展示出
1.1.6  prometheus如何更好的监控k8s？对于Kubernetes而言，我们可以把当中所有的资源分为几类：1、基础设施层（Node）：集群节点，为整个集群和应用提供运行时资源2、容器基础设施（Container）：为应用提供运行时环境3、用户应用（Pod）：Pod中会包含一组容器，它们一起工作，并且对外提供一个（或者一组）功能4、内部服务负载均衡（Service）：在集群内，通过Service在集群暴露应用功能，集群内应用和应用之间访问时提供内部的负载均衡5、外部访问入口（Ingress）：通过Ingress提供集群外的访问入口，从而可以使外部客户端能够访问到部署在Kubernetes集群内的服务因此，在不考虑Kubernetes自身组件的情况下，如果要构建一个完整的监控体系，我们应该考虑，以下5个方面：1、集群节点状态监控：从集群中各节点的kubelet服务获取节点的基本运行状态；2、集群节点资源用量监控：通过Daemonset的形式在集群中各个节点部署Node Exporter采集节点的资源使用情况；3、节点中运行的容器监控：通过各个节点中kubelet内置的cAdvisor中获取个节点中所有容器的运行状态和资源使用情况；4、从黑盒监控的角度在集群中部署Blackbox Exporter探针服务，检测Service和Ingress的可用性；5、如果在集群中部署的应用程序本身内置了对Prometheus的监控支持，那么我们还应该找到相应的Pod实例，并从该Pod实例中获取其内部运行状态的监控指标。

1.2  安装采集节点资源指标组件node-exporternode-exporter是什么？采集机器（物理机、虚拟机、云主机等）的监控指标数据，能够采集到的指标包括CPU, 内存，磁盘，网络，文件数等信息。安装node-exporter组件，在k8s集群的控制节点操作[root@master1 ~]# kubectl create ns monitor-sanamespace/monitor-sa created把课件里的node-exporter.tar.gz镜像压缩包上传到k8s的各个节点，手动解压：docker load -i node-exporter.tar.gznode-export.yaml文件在课件，可自行上传到自己k8s的控制节点，内容如下：[root@master1 ~]# cat node-export.yaml #通过kubectl apply更新node-exporter[root@master1 ~]# kubectl apply -f node-export.yamldaemonset.apps/node-exporter created#查看node-exporter是否部署成功[root@master1 ~]# kubectl get pods -n monitor-saNAME                  READY   STATUS    RESTARTS   AGEnode-exporter-7cjhw   1/1     Running   0          22snode-exporter-8m2fp   1/1     Running   0          22snode-exporter-c6sdq   1/1     Running   0          22s通过node-exporter采集数据curl  http://主机ip:9100/metrics#node-export默认的监听端口是9100，可以看到当前主机获取到的所有监控数据 curl http://192.168.40.130:9100/metrics | grep node_cpu_seconds显示192.168.40.130主机cpu的使用情况# HELP node_cpu_seconds_total Seconds the cpus spent in each mode.# TYPE node_cpu_seconds_total counternode_cpu_seconds_total{cpu="0",mode="idle"} 72963.37node_cpu_seconds_total{cpu="0",mode="iowait"} 9.35node_cpu_seconds_total{cpu="0",mode="irq"} 0node_cpu_seconds_total{cpu="0",mode="nice"} 0node_cpu_seconds_total{cpu="0",mode="softirq"} 151.4node_cpu_seconds_total{cpu="0",mode="steal"} 0node_cpu_seconds_total{cpu="0",mode="system"} 656.12node_cpu_seconds_total{cpu="0",mode="user"} 267.1
#HELP：解释当前指标的含义，上面表示在每种模式下node节点的cpu花费的时间，以s为单位#TYPE：说明当前指标的数据类型，上面是counter类型node_cpu_seconds_total{cpu="0",mode="idle"} ：cpu0上idle进程占用CPU的总时间，CPU占用时间是一个只增不减的度量指标，从类型中也可以看出node_cpu的数据类型是counter（计数器）counter计数器：只是采集递增的指标
curl http://192.168.40.130:9100/metrics | grep node_load# HELP node_load1 1m load average.# TYPE node_load1 gaugenode_load1 0.1node_load1该指标反映了当前主机在最近一分钟以内的负载情况，系统的负载情况会随系统资源的使用而变化，因此node_load1反映的是当前状态，数据可能增加也可能减少，从注释中可以看出当前指标类型为gauge（标准尺寸）gauge标准尺寸：统计的指标可增加可减少
1.3  在k8s集群中安装Prometheus server服务1.3.1 创建sa账号#在k8s集群的控制节点操作，创建一个sa账号  [root@master1 ~]# kubectl create serviceaccount monitor -n monitor-saserviceaccount/monitor created#把sa账号monitor通过clusterrolebing绑定到clusterrole上[root@master1 ~]# kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin  --serviceaccount=monitor-sa:monitor1.3.2 创建数据目录#在node1作节点创建存储数据的目录：[root@node1 ~]# mkdir  /data[root@node1 ~]# chmod  777 /data/1.3.3 安装prometheus服务  以下步骤均在k8s集群的控制节点操作：创建一个configmap存储卷，用来存放prometheus配置信息prometheus-cfg.yaml文件在课件，可自行上传到自己k8s的控制节点，内容如下：[root@master1 ~]# cat  prometheus-cfg.yaml#通过kubectl apply更新configmap[root@master1 ~]# kubectl apply  -f  prometheus-cfg.yamlconfigmap/prometheus-config created通过deployment部署prometheus安装prometheus server需要的镜像prometheus-2-2-1.tar.gz在课件，上传到k8s的工作节点node1上，手动解压：docker load -i prometheus-2-2-1.tar.gzprometheus-deploy.yaml文件在课件，上传到自己的k8s的控制节点，内容如下：[root@master1 ~]# cat prometheus-deploy.yaml 注意：在上面的prometheus-deploy.yaml文件有个nodeName字段，这个就是用来指定创建的这个prometheus的pod调度到哪个节点上，我们这里让nodeName=node1，也即是让pod调度到node1节点上，因为node1节点我们创建了数据目录/data，所以大家记住：你在k8s集群的哪个节点创建/data，就让pod调度到哪个节点。#通过kubectl apply更新prometheus[root@master1 ~]# kubectl apply -f prometheus-deploy.yamldeployment.apps/prometheus-server created#查看prometheus是否部署成功[root@master1 ~]# kubectl get pods -n monitor-saNAME                                 READY   STATUS    RESTARTS   AGEnode-exporter-7cjhw                  1/1     Running   0          6m33snode-exporter-8m2fp                  1/1     Running   0          6m33snode-exporter-c6sdq                  1/1     Running   0          6m33sprometheus-server-6fffccc6c9-bhbpz   1/1     Running   0          26s给prometheus pod创建一个serviceprometheus-svc.yaml文件在课件，可上传到k8s的控制节点，内容如下：cat  prometheus-svc.yaml ---apiVersion: v1kind: Servicemetadata:  name: prometheus  namespace: monitor-sa  labels:    app: prometheusspec:  type: NodePort  ports:    - port: 9090      targetPort: 9090      protocol: TCP  selector:    app: prometheus    component: server#通过kubectl apply 更新service[root@master1 ~]# kubectl  apply -f prometheus-svc.yamlservice/prometheus created#查看service在物理机映射的端口[root@master1 ~]# kubectl get svc -n monitor-saNAME         TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGEprometheus   NodePort   10.103.98.225   <none>        9090:30009/TCP   27s通过上面可以看到service在宿主机上映射的端口是30009，这样我们访问k8s集群的控制节点的ip:30009，就可以访问到prometheus的web ui界面了#访问prometheus web ui界面火狐浏览器输入如下地址：http://192.168.40.130:30009/graph可看到如下页面：

1.4  安装和配置可视化UI界面Grafana安装Grafana需要的镜像heapster-grafana-amd64_v5_0_4.tar.gz在课件里，把镜像上传到k8s的各个控制节点和k8s的各个工作节点，然后在各个节点手动解压：docker load -i heapster-grafana-amd64_v5_0_4.tar.gzgrafana.yaml文件在课件里，可上传到k8s的控制节点，内容如下：[root@master1 ~]# cat  grafana.yaml apiVersion: apps/v1kind: Deploymentmetadata:  name: monitoring-grafana  namespace: kube-systemspec:  replicas: 1  selector:    matchLabels:      task: monitoring      k8s-app: grafana  template:    metadata:      labels:        task: monitoring        k8s-app: grafana    spec:      containers:      - name: grafana        image: k8s.gcr.io/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:        - name: INFLUXDB_HOST          value: monitoring-influxdb        - name: GF_SERVER_HTTP_PORT          value: "3000"          # The following env variables are required to make Grafana accessible via          # the kubernetes api-server proxy. On production clusters, we recommend          # removing these env variables, setup auth for grafana, and expose the grafana          # service using a LoadBalancer or a public IP.        - 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          # If you're only using the API Server proxy, set this value instead:          # value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy          value: /      volumes:      - name: ca-certificates        hostPath:          path: /etc/ssl/certs      - name: grafana-storage        emptyDir: {}---apiVersion: v1kind: Servicemetadata:  labels:    # For use as a Cluster add-on (https://github.com/kubernetes/kubernetes/tree/master/cluster/addons)    # If you are NOT using this as an addon, you should comment out this line.    kubernetes.io/cluster-service: 'true'    kubernetes.io/name: monitoring-grafana  name: monitoring-grafana  namespace: kube-systemspec:  # In a production setup, we recommend accessing Grafana through an external Loadbalancer  # or through a public IP.  # type: LoadBalancer  # You could also use NodePort to expose the service at a randomly-generated port  # type: NodePort  ports:  - port: 80    targetPort: 3000  selector:    k8s-app: grafana  type: NodePort#更新yaml文件[root@master1 ~]# kubectl apply -f grafana.yamldeployment.apps/monitoring-grafana createdservice/monitoring-grafana created#验证是否安装成功[root@master1 ~]# kubectl get pods -n kube-system| grep monitormonitoring-grafana-675798bf47-4rp2b         1/1     Running            0          #查看grafana前端的service[root@master1 ~]# kubectl get svc -n kube-system | grep grafanamonitoring-grafana     NodePort    10.100.56.76     <none>        80:30989/TCP            #登陆grafana，在浏览器访问192.168.40.130:30989可看到如下界面：

#配置grafana界面
开始配置grafana的web界面：
选择Create your first data source

出现如下

Name:Prometheus

Type:Prometheus

HTTP 处的URL如下：

http://prometheus.monitor-sa.svc:9090

配置好的整体页面如下：

点击左下角Save& Test，出现如下Data source is working，说明prometheus数据源成功的被grafana接入了:

导入监控模板，可在如下链接搜索
https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes

可直接导入node_exporter.json监控模板，这个可以把node节点指标显示出来

node_exporter.json在课件里，也可直接导入docker_rev1.json，这个可以把容器资源指标显示出来，node_exporter.json和docker_rev1.json都在课件里

怎么导入监控模板，按如下步骤

上面Save& Test测试没问题之后，就可以返回Grafana主页面

点击左侧+号下面的Import

出现如下界面：

选择Upload json file，出现如下

选择一个本地的json文件，我们选择的是上面让大家下载的node_exporter.json这个文件，选择之后出现如下：

注：箭头标注的地方Name后面的名字是node_exporter.json定义的

Prometheus后面需要变成Prometheus，然后再点击Import，就可以出现如下界面：

导入docker_rev1.json监控模板，步骤和上面导入node_exporter.json步骤一样，导入之后显示如下：

1.5  kube-state-metrics组件解读1.5.1  什么是kube-state-metrics？kube-state-metrics通过监听API Server生成有关资源对象的状态指标，比如Deployment、Node、Pod，需要注意的是kube-state-metrics只是简单的提供一个metrics数据，并不会存储这些指标数据，所以我们可以使用Prometheus来抓取这些数据然后存储，主要关注的是业务相关的一些元数据，比如Deployment、Pod、副本状态等；调度了多少个replicas？现在可用的有几个？多少个Pod是running/stopped/terminated状态？Pod重启了多少次？我有多少job在运行中。1.5.2  安装和配置kube-state-metrics创建sa，并对sa授权在k8s的控制节点生成一个kube-state-metrics-rbac.yaml文件，kube-state-metrics-rbac.yaml文件在课件，大家自行下载到k8s的控制节点即可，内容如下：[root@master1 ~]# cat  kube-state-metrics-rbac.yaml ---apiVersion: v1kind: ServiceAccountmetadata:  name: kube-state-metrics  namespace: kube-system---apiVersion: rbac.authorization.k8s.io/v1kind: ClusterRolemetadata:  name: kube-state-metricsrules:- apiGroups: [""]  resources: ["nodes", "pods", "services", "resourcequotas", "replicationcontrollers", "limitranges", "persistentvolumeclaims", "persistentvolumes", "namespaces", "endpoints"]  verbs: ["list", "watch"]- apiGroups: ["extensions"]  resources: ["daemonsets", "deployments", "replicasets"]  verbs: ["list", "watch"]- apiGroups: ["apps"]  resources: ["statefulsets"]  verbs: ["list", "watch"]- apiGroups: ["batch"]  resources: ["cronjobs", "jobs"]  verbs: ["list", "watch"]- apiGroups: ["autoscaling"]  resources: ["horizontalpodautoscalers"]  verbs: ["list", "watch"]---apiVersion: rbac.authorization.k8s.io/v1kind: ClusterRoleBindingmetadata:  name: kube-state-metricsroleRef:  apiGroup: rbac.authorization.k8s.io  kind: ClusterRole  name: kube-state-metricssubjects:- kind: ServiceAccount  name: kube-state-metrics  namespace: kube-system通过kubectl apply更新yaml文件[root@master1 ~]# kubectl apply -f kube-state-metrics-rbac.yaml serviceaccount/kube-state-metrics createdclusterrole.rbac.authorization.k8s.io/kube-state-metrics createdclusterrolebinding.rbac.authorization.k8s.io/kube-state-metrics created安装kube-state-metrics组件安装kube-state-metrics组件需要的镜像在课件，可上传到k8s各个工作节点，手动解压：docker load -i kube-state-metrics_1_9_0.tar.gz在k8s的master1节点生成一个kube-state-metrics-deploy.yaml文件，kube-state-metrics-deploy.yaml在课件，可自行下载，内容如下：[root@master1 ~]# cat kube-state-metrics-deploy.yaml apiVersion: apps/v1kind: Deploymentmetadata:  name: kube-state-metrics  namespace: kube-systemspec:  replicas: 1  selector:    matchLabels:      app: kube-state-metrics  template:    metadata:      labels:        app: kube-state-metrics    spec:      serviceAccountName: kube-state-metrics      containers:      - name: kube-state-metrics        image: quay.io/coreos/kube-state-metrics:v1.9.0        ports:        - containerPort: 8080通过kubectl apply更新yaml文件[root@master1 ~]# kubectl apply -f kube-state-metrics-deploy.yaml deployment.apps/kube-state-metrics created查看kube-state-metrics是否部署成功[root@master1 ~]# kubectl get pods -n kube-system -l app=kube-state-metricsNAME                                  READY   STATUS    RESTARTS   AGEkube-state-metrics-58d4957bc5-9thsw   1/1     Running   0          30s创建service在k8s的控制节点生成一个kube-state-metrics-svc.yaml文件，kube-state-metrics-svc.yaml文件在课件，可上传到k8s的控制节点，内容如下：[root@master1 ~]# cat kube-state-metrics-svc.yaml apiVersion: v1kind: Servicemetadata:  annotations:    prometheus.io/scrape: 'true'  name: kube-state-metrics  namespace: kube-system  labels:    app: kube-state-metricsspec:  ports:  - name: kube-state-metrics    port: 8080    protocol: TCP  selector:    app: kube-state-metrics通过kubectl apply更新yaml[root@master1 ~]# kubectl apply -f kube-state-metrics-svc.yaml service/kube-state-metrics created查看service是否创建成功[root@master1 ~]# kubectl get svc -n kube-system | grep kube-state-metricskube-state-metrics     ClusterIP   10.105.160.224   <none>        8080/TCP
在grafana web界面导入Kubernetes Cluster (Prometheus)-1577674936972.json和Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json，Kubernetes Cluster (Prometheus)-1577674936972.json和Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json文件在课件导入Kubernetes Cluster (Prometheus)-1577674936972.json之后出现如下页面

在grafana web界面导入Kubernetes cluster monitoring(via Prometheus) (k8s 1.16)-1577691996738.json，出现如下页面

1.6  安装和配置Alertmanager-发送报警到qq邮箱在k8s的master1节点创建alertmanager-cm.yaml文件，alertmanager-cm.yaml文件在课件，可直接从课件传到k8s的master1节点，内容如下：[root@master1 ~]# cat alertmanager-cm.yaml kind: ConfigMapapiVersion: v1metadata:  name: alertmanager  namespace: monitor-sadata:  alertmanager.yml: |-    global:      resolve_timeout: 1m      smtp_smarthost: 'smtp.163.com:25'      smtp_from: '1501157****@163.com'      smtp_auth_username: '1501157****'      smtp_auth_password: ' FLWYKIDBNBAIFFXV      smtp_require_tls: false    route:  #用于配置告警分发策略      group_by: [alertname] # 采用哪个标签来作为分组依据      group_wait: 10s       # 组告警等待时间。也就是告警产生后等待10s，如果有同组告警一起发出      group_interval: 10s   # 两组告警的间隔时间      repeat_interval: 10m    # 重复告警的间隔时间，减少相同邮件的发送频率      receiver: default-receiver  # 设置默认接收人    receivers:    - name: 'default-receiver'      email_configs:      - to: '1980570***@qq.com'        send_resolved: truealertmanager配置文件解释说明：smtp_smarthost: 'smtp.163.com:25'#用于发送邮件的邮箱的SMTP服务器地址+端口smtp_from: '1501157****@163.com'#这是指定从哪个邮箱发送报警smtp_auth_username: '1501157****'#这是发送邮箱的认证用户，不是邮箱名smtp_auth_password: 'BDBPRMLNZGKWRFJP'#这是发送邮箱的授权码而不是登录密码email_configs:   - to: '1980570***@qq.com'#to后面指定发送到哪个邮箱，我发送到我的qq邮箱，大家需要写自己的邮箱地址，不应该跟smtp_from的邮箱名字重复#通过kubectl apply 更新文件[root@master1 ~]# kubectl apply -f alertmanager-cm.yaml configmap/alertmanager created在k8s的master1节点生成一个prometheus-alertmanager-cfg.yaml文件，prometheus-alertmanager-cfg.yaml文件在课件，上传到k8s的master1节点，内容如下：[root@master1 ~]# cat prometheus-alertmanager-cfg.yaml kind: ConfigMapapiVersion: v1metadata:  labels:    app: prometheus  name: prometheus-config  namespace: monitor-sadata:  prometheus.yml: |    rule_files:    - /etc/prometheus/rules.yml    alerting:      alertmanagers:      - static_configs:        - targets: ["localhost:9093"]    global:      scrape_interval: 15s      scrape_timeout: 10s      evaluation_interval: 1m    scrape_configs:    - job_name: 'kubernetes-node'      kubernetes_sd_configs:      - role: node      relabel_configs:      - source_labels: [__address__]        regex: '(.*):10250'        replacement: '${1}:9100'        target_label: __address__        action: replace      - action: labelmap        regex: __meta_kubernetes_node_label_(.+)    - job_name: 'kubernetes-node-cadvisor'      kubernetes_sd_configs:      - role:  node      scheme: https      tls_config:        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token      relabel_configs:      - action: labelmap        regex: __meta_kubernetes_node_label_(.+)      - target_label: __address__        replacement: kubernetes.default.svc:443      - source_labels: [__meta_kubernetes_node_name]        regex: (.+)        target_label: __metrics_path__        replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor    - job_name: 'kubernetes-apiserver'      kubernetes_sd_configs:      - role: endpoints      scheme: https      tls_config:        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token      relabel_configs:      - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]        action: keep        regex: default;kubernetes;https    - job_name: 'kubernetes-service-endpoints'      kubernetes_sd_configs:      - role: endpoints      relabel_configs:      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]        action: keep        regex: true      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]        action: replace        target_label: __scheme__        regex: (https?)      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]        action: replace        target_label: __metrics_path__        regex: (.+)      - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]        action: replace        target_label: __address__        regex: ([^:]+)(?::\d+)?;(\d+)        replacement: $1:$2      - action: labelmap        regex: __meta_kubernetes_service_label_(.+)      - source_labels: [__meta_kubernetes_namespace]        action: replace        target_label: kubernetes_namespace      - source_labels: [__meta_kubernetes_service_name]        action: replace        target_label: kubernetes_name     - job_name: kubernetes-pods      kubernetes_sd_configs:      - role: pod      relabel_configs:      - action: keep        regex: true        source_labels:        - __meta_kubernetes_pod_annotation_prometheus_io_scrape      - action: replace        regex: (.+)        source_labels:        - __meta_kubernetes_pod_annotation_prometheus_io_path        target_label: __metrics_path__      - action: replace        regex: ([^:]+)(?::\d+)?;(\d+)        replacement: $1:$2        source_labels:        - __address__        - __meta_kubernetes_pod_annotation_prometheus_io_port        target_label: __address__      - action: labelmap        regex: __meta_kubernetes_pod_label_(.+)      - action: replace        source_labels:        - __meta_kubernetes_namespace        target_label: kubernetes_namespace      - action: replace        source_labels:        - __meta_kubernetes_pod_name        target_label: kubernetes_pod_name    - job_name: 'kubernetes-schedule'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10251']    - job_name: 'kubernetes-controller-manager'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10252']    - job_name: 'kubernetes-kube-proxy'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10249','192.168.40.131:10249']    - job_name: 'kubernetes-etcd'      scheme: https      tls_config:        ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt        cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt        key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:2379']  rules.yml: |    groups:    - name: example      rules:      - alert: kube-proxy的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"      - alert:  kube-proxy的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"      - alert: scheduler的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"      - alert:  scheduler的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"      - alert: controller-manager的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"      - alert:  controller-manager的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"      - alert: apiserver的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"      - alert:  apiserver的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"      - alert: etcd的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"      - alert:  etcd的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"      - alert: kube-state-metrics的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"          value: "{{ $value }}%"          threshold: "80%"            - alert: kube-state-metrics的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"          value: "{{ $value }}%"          threshold: "90%"            - alert: coredns的cpu使用率大于80%        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"          value: "{{ $value }}%"          threshold: "80%"            - alert: coredns的cpu使用率大于90%        expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"          value: "{{ $value }}%"          threshold: "90%"            - alert: kube-proxy打开句柄数>600        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 600        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"          value: "{{ $value }}"      - alert: kube-proxy打开句柄数>1000        expr: process_open_fds{job=~"kubernetes-kube-proxy"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"          value: "{{ $value }}"      - alert: kubernetes-schedule打开句柄数>600        expr: process_open_fds{job=~"kubernetes-schedule"}  > 600        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"          value: "{{ $value }}"      - alert: kubernetes-schedule打开句柄数>1000        expr: process_open_fds{job=~"kubernetes-schedule"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"          value: "{{ $value }}"      - alert: kubernetes-controller-manager打开句柄数>600        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 600        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"          value: "{{ $value }}"      - alert: kubernetes-controller-manager打开句柄数>1000        expr: process_open_fds{job=~"kubernetes-controller-manager"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"          value: "{{ $value }}"      - alert: kubernetes-apiserver打开句柄数>600        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 600        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"          value: "{{ $value }}"      - alert: kubernetes-apiserver打开句柄数>1000        expr: process_open_fds{job=~"kubernetes-apiserver"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"          value: "{{ $value }}"      - alert: kubernetes-etcd打开句柄数>600        expr: process_open_fds{job=~"kubernetes-etcd"}  > 600        for: 2s        labels:          severity: warnning        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"          value: "{{ $value }}"      - alert: kubernetes-etcd打开句柄数>1000        expr: process_open_fds{job=~"kubernetes-etcd"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"          value: "{{ $value }}"      - alert: coredns        expr: process_open_fds{k8s_app=~"kube-dns"}  > 600        for: 2s        labels:          severity: warnning         annotations:          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"          value: "{{ $value }}"      - alert: coredns        expr: process_open_fds{k8s_app=~"kube-dns"}  > 1000        for: 2s        labels:          severity: critical        annotations:          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"          value: "{{ $value }}"      - alert: kube-proxy        expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: scheduler        expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: kubernetes-controller-manager        expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: kubernetes-apiserver        expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: kubernetes-etcd        expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: kube-dns        expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"}  > 2000000000        for: 2s        labels:          severity: warnning        annotations:          description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"          value: "{{ $value }}"      - alert: HttpRequestsAvg        expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m]))  > 1000        for: 2s        labels:          team: admin        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"          value: "{{ $value }}"          threshold: "1000"         - alert: Pod_restarts        expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0        for: 2s        labels:          severity: warnning        annotations:          description: "在{{$labels.namespace}}名称空间下发现{{$labels.pod}}这个pod下的容器{{$labels.container}}被重启,这个监控指标是由{{$labels.instance}}采集的"          value: "{{ $value }}"          threshold: "0"      - alert: Pod_waiting        expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1        for: 2s        labels:          team: admin        annotations:          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"          value: "{{ $value }}"          threshold: "1"         - alert: Pod_terminated        expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1        for: 2s        labels:          team: admin        annotations:          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"          value: "{{ $value }}"          threshold: "1"      - alert: Etcd_leader        expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0        for: 2s        labels:          team: admin        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"          value: "{{ $value }}"          threshold: "0"      - alert: Etcd_leader_changes        expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0        for: 2s        labels:          team: admin        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"          value: "{{ $value }}"          threshold: "0"      - alert: Etcd_failed        expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0        for: 2s        labels:          team: admin        annotations:          description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"          value: "{{ $value }}"          threshold: "0"      - alert: Etcd_db_total_size        expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000        for: 2s        labels:          team: admin        annotations:          description: "组件{{$labels.job}}({{$labels.instance}})：db空间超过10G"          value: "{{ $value }}"          threshold: "10G"      - alert: Endpoint_ready        expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1        for: 2s        labels:          team: admin        annotations:          description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.endpoint}}不可用"          value: "{{ $value }}"          threshold: "1"    - name: 物理节点状态-监控告警      rules:      - alert: 物理节点cpu使用率        expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90        for: 2s        labels:          severity: ccritical        annotations:          summary: "{{ $labels.instance }}cpu使用率过高"          description: "{{ $labels.instance }}的cpu使用率超过90%,当前使用率[{{ $value }}],需要排查处理"       - alert: 物理节点内存使用率        expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90        for: 2s        labels:          severity: critical        annotations:          summary: "{{ $labels.instance }}内存使用率过高"          description: "{{ $labels.instance }}的内存使用率超过90%,当前使用率[{{ $value }}],需要排查处理"      - alert: InstanceDown        expr: up == 0        for: 2s        labels:          severity: critical        annotations:             summary: "{{ $labels.instance }}: 服务器宕机"          description: "{{ $labels.instance }}: 服务器延时超过2分钟"      - alert: 物理节点磁盘的IO性能        expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60        for: 2s        labels:          severity: critical        annotations:          summary: "{{$labels.mountpoint}} 流入磁盘IO使用率过高！"          description: "{{$labels.mountpoint }} 流入磁盘IO大于60%(目前使用:{{$value}})"      - alert: 入网流量带宽        expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400        for: 2s        labels:          severity: critical        annotations:          summary: "{{$labels.mountpoint}} 流入网络带宽过高！"          description: "{{$labels.mountpoint }}流入网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"      - alert: 出网流量带宽        expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400        for: 2s        labels:          severity: critical        annotations:          summary: "{{$labels.mountpoint}} 流出网络带宽过高！"          description: "{{$labels.mountpoint }}流出网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"      - alert: TCP会话        expr: node_netstat_Tcp_CurrEstab > 1000        for: 2s        labels:          severity: critical        annotations:          summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高！"          description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"      - alert: 磁盘容量        expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80        for: 2s        labels:          severity: critical        annotations:          summary: "{{$labels.mountpoint}} 磁盘分区使用率过高！"          description: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$value}}%)"
注意：配置文件解释说明- job_name: 'kubernetes-schedule'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10251']  #master1节点的ip:schedule端口    - job_name: 'kubernetes-controller-manager'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10252'] #master1节点的ip:controller-manager端口    - job_name: 'kubernetes-kube-proxy'      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:10249','192.168.40.131:10249']#master1和node1节点的ip:kube-proxy端口    - job_name: 'kubernetes-etcd'      scheme: https      tls_config:        ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt        cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt        key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key      scrape_interval: 5s      static_configs:      - targets: ['192.168.40.130:2379']#master1节点的ip:etcd端口
#更新资源清单文件[root@master1 ~]# kubectl delete -f prometheus-cfg.yaml configmap "prometheus-config" deleted[root@master1 ~]# kubectl apply -f prometheus-alertmanager-cfg.yaml configmap/prometheus-config created
安装prometheus和alertmanager需要把alertmanager.tar.gz镜像包上传的k8s的各个节点，手动解压：docker load -i alertmanager.tar.gz在k8s的master1节点生成一个prometheus-alertmanager-deploy.yaml文件，prometheus-alertmanager-deploy.yaml文件在课件里，可自行上传到k8s master1节点上，内容如下：[root@master1 ~]# cat prometheus-alertmanager-deploy.yaml ---apiVersion: apps/v1kind: Deploymentmetadata:  name: prometheus-server  namespace: monitor-sa  labels:    app: prometheusspec:  replicas: 1  selector:    matchLabels:      app: prometheus      component: server    #matchExpressions:    #- {key: app, operator: In, values: [prometheus]}    #- {key: component, operator: In, values: [server]}  template:    metadata:      labels:        app: prometheus        component: server      annotations:        prometheus.io/scrape: 'false'    spec:      nodeName: node1      serviceAccountName: monitor      containers:      - name: prometheus        image: prom/prometheus:v2.2.1        imagePullPolicy: IfNotPresent        command:        - "/bin/prometheus"        args:        - "--config.file=/etc/prometheus/prometheus.yml"        - "--storage.tsdb.path=/prometheus"        - "--storage.tsdb.retention=24h"        - "--web.enable-lifecycle"        ports:        - containerPort: 9090          protocol: TCP        volumeMounts:        - mountPath: /etc/prometheus          name: prometheus-config        - mountPath: /prometheus/          name: prometheus-storage-volume        - name: k8s-certs          mountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/      - name: alertmanager        image: prom/alertmanager:v0.14.0        imagePullPolicy: IfNotPresent        args:        - "--config.file=/etc/alertmanager/alertmanager.yml"        - "--log.level=debug"        ports:        - containerPort: 9093          protocol: TCP          name: alertmanager        volumeMounts:        - name: alertmanager-config          mountPath: /etc/alertmanager        - name: alertmanager-storage          mountPath: /alertmanager        - name: localtime          mountPath: /etc/localtime      volumes:        - name: prometheus-config          configMap:            name: prometheus-config        - name: prometheus-storage-volume          hostPath:           path: /data           type: Directory        - name: k8s-certs          secret:           secretName: etcd-certs        - name: alertmanager-config          configMap:            name: alertmanager        - name: alertmanager-storage          hostPath:           path: /data/alertmanager           type: DirectoryOrCreate        - name: localtime          hostPath:           path: /usr/share/zoneinfo/Asia/Shanghai
注意：配置文件指定了nodeName: node1，这个位置要写你自己环境的node节点名字




生成一个etcd-certs，这个在部署prometheus需要[root@master1 ~]# kubectl -n monitor-sa create secret generic etcd-certs --from-file=/etc/kubernetes/pki/etcd/server.key  --from-file=/etc/kubernetes/pki/etcd/server.crt --from-file=/etc/kubernetes/pki/etcd/ca.crt
通过kubectl apply更新yaml文件[root@master1 ~]# kubectl delete -f prometheus-deploy.yaml[root@master1 ~]# kubectl apply -f prometheus-alertmanager-deploy.yaml deployment.apps/prometheus-server created
#查看prometheus是否部署成功kubectl get pods -n monitor-sa | grep prometheus显示如下，可看到pod状态是running，说明prometheus部署成功prometheus-server-6c46df5b6-4l9b4   2/2     Running   0          38s
在k8s的master1节点生成一个alertmanager-svc.yaml文件，alertmanager-svc.yaml文件在课件里，可以手动上传到k8s的master1节点，内容如下：[root@master1 ~]# cat alertmanager-svc.yaml ---apiVersion: v1kind: Servicemetadata:  labels:    name: prometheus    kubernetes.io/cluster-service: 'true'  name: alertmanager  namespace: monitor-saspec:  ports:  - name: alertmanager    nodePort: 30066    port: 9093    protocol: TCP    targetPort: 9093  selector:    app: prometheus  sessionAffinity: None  type: NodePort#通过kubectl apply 更新yaml文件[root@master1 ~]# kubectl apply -f alertmanager-svc.yaml service/alertmanager created#查看service在物理机上映射的端口[root@master1 ~]# kubectl get svc -n monitor-saNAME           TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGEalertmanager   NodePort   10.98.142.161   <none>        9093:30066/TCP   56sprometheus     NodePort   10.103.98.225   <none>        9090:30009/TCP   56m
注意：上面可以看到prometheus的service暴漏的端口是30009，alertmanager的service暴露的端口是30066
访问prometheus的web界面点击status->targets，可看到如下

从上面可以发现kubernetes-controller-manager和kubernetes-schedule都显示连接不上对应的端口

可按如下方法处理；vim /etc/kubernetes/manifests/kube-scheduler.yaml修改如下内容：把--bind-address=127.0.0.1变成--bind-address=192.168.40.130把httpGet:字段下的hosts由127.0.0.1变成192.168.40.130把—port=0删除
#注意：192.168.40.130是k8s的控制节点master1节点ip
vim /etc/kubernetes/manifests/kube-controller-manager.yaml把--bind-address=127.0.0.1变成--bind-address=192.168.40.130把httpGet:字段下的hosts由127.0.0.1变成192.168.40.130把—port=0删除
修改之后在k8s各个节点执行systemctl restart kubelet
kubectl get cs 显示如下:NAME                 STATUS    MESSAGE             ERRORcontroller-manager   Healthy   ok                  scheduler            Healthy   ok                  etcd-0               Healthy   {"health":"true"}
ss -antulp | grep :10251ss -antulp | grep :10252可以看到相应的端口已经被物理机监听了点击status->targets，可看到如下

kubernetes-kube-proxy显示如下：

是因为kube-proxy默认端口10249是监听在127.0.0.1上的，需要改成监听到物理节点上，按如下方法修改，线上建议在安装k8s的时候就做修改，这样风险小一些：kubectl edit configmap kube-proxy -n kube-system把metricsBindAddress这段修改成metricsBindAddress: 0.0.0.0:10249然后重新启动kube-proxy这个podkubectl get pods -n kube-system | grep kube-proxy |awk '{print $1}' | xargs kubectl delete pods -n kube-systemss  -antulp |grep :10249可显示如下[root@k8s-master ~]# ss -antulp | grep :10249tcp    LISTEN     0      128    [::]:10249    
点击Alerts，可看到如下