前言
本篇文章介绍k8s集群中部署prometheus、grafana、alertmanager，并且配置prometheus的动态、静态服务发现，实现对容器、物理节点、service、pod等资源指标监控，并在Grafana的web界面展示prometheus的监控指标，然后通过配置自定义告警规则，通过alertmanager实现qq、钉钉、微信报警，文章内容较多，大概1.5万以上字数，可以先关注和转发，在慢慢学习。
prometheus简介
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。
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。
prometheus组件介绍
1.Prometheus Server: 用于收集和存储时间序列数据。
2.Client Library: 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
4.Alertmanager: 从 Prometheus server 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
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。
prometheus架构图
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，Prometheus server由三个部分组成，Retrieval，Storage，PromQL
Retrieval负责在活跃的target主机上抓取监控指标数据
Storage存储主要是把采集到的数据存储到磁盘中
PromQL是Prometheus提供的查询语言模块。
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alertmanager
4.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
安装node-exporter组件
机器规划：
我的实验环境使用的k8s集群是一个master节点和一个node节点
master节点的机器ip是192.168.0.6，主机名是master1
node节点的机器ip是192.168.0.56，主机名是node1
master高可用集群安装可参考如下文章：
k8s1.18多master节点高可用集群安装-超详细中文官方文档
node-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master1节点操作
cat >node-export.yaml <<EOFapiVersion: apps/v1kind: DaemonSetmetadata: name: node-exporter namespace: monitor-sa labels: name: node-exporterspec: selector: matchLabels: name: node-exporter template: metadata: labels: name: node-exporter spec: hostPID: true hostIPC: true hostNetwork: true containers: - name: node-exporter image: prom/node-exporter:v0.16.0 ports: - containerPort: 9100 resources: requests: cpu: 0.15 securityContext: privileged: true args: - --path.procfs - /host/proc - --path.sysfs - /host/sys - --collector.filesystem.ignored-mount-points - '"^/(sys|proc|dev|host|etc)($|/)"' volumeMounts: - name: dev mountPath: /host/dev - name: proc mountPath: /host/proc - name: sys mountPath: /host/sys - name: rootfs mountPath: /rootfs tolerations: - key: "node-role.kubernetes.io/master" operator: "Exists" effect: "NoSchedule" volumes: - name: proc hostPath: path: /proc - name: dev hostPath: path: /dev - name: sys hostPath: path: /sys - name: rootfs hostPath: path: /EOF
#通过kubectl apply更新node-exporter
kubectl apply -f node-export.yaml
#查看node-exporter是否部署成功
kubectl get pods -n monitor-sa
显示如下，看到pod的状态都是running，说明部署成功
NAME READY STATUS RESTARTS AGEnode-exporter-9qpkd 1/1 Running 0 89snode-exporter-zqmnk 1/1 Running 0 89s
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，截取一部分，如下
# 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"} 56136.98# HELP node_load1 1m load average.# TYPE node_load1 gaugenode_load1 0.58#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_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）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gauge标准尺寸：统计的指标可增加可减少
k8s集群中部署prometheus
1.创建namespace、sa账号，在k8s集群的master节点操作
#创建一个monitor-sa的名称空间
kubectl create ns monitor-sa
#创建一个sa账号
kubectl create serviceaccount monitor -n monitor-sa
#把sa账号monitor通过clusterrolebing绑定到clusterrole上
kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
2.创建数据目录
#在k8s集群的任何一个node节点操作，因为我的k8s集群只有一个node节点node1，所以我在node1上操作如下命令：
mkdir /data
chmod 777 /data/
3.安装prometheus，以下步骤均在在k8s集群的master1节点操作
1）创建一个configmap存储卷，用来存放prometheus配置信息
cat >prometheus-cfg.yaml <<EOF---kind: ConfigMapapiVersion: v1metadata: labels: app: prometheus name: prometheus-config namespace: monitor-sadata: prometheus.yml: | 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 EOF
注意：通过上面命令生成的promtheus-cfg.yaml文件会有一些问题，$1和$2这种变量在文件里没有，需要在k8s的master1节点打开promtheus-cfg.yaml文件，手动把$1和$2这种变量写进文件里，promtheus-cfg.yaml文件需要手动修改部分如下：
22行的replacement: ':9100'变成replacement: '${1}:9100'42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成 replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor73行的replacement: 变成replacement: $1:$2
#通过kubectl apply更新configmap
kubectl apply -f prometheus-cfg.yaml
2）通过deployment部署prometheus
cat >prometheus-deploy.yaml <<EOF---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: - prometheus - --config.file=/etc/prometheus/prometheus.yml - --storage.tsdb.path=/prometheus - --storage.tsdb.retention=720h ports: - containerPort: 9090 protocol: TCP volumeMounts: - mountPath: /etc/prometheus/prometheus.yml name: prometheus-config subPath: prometheus.yml - mountPath: /prometheus/ name: prometheus-storage-volume volumes: - name: prometheus-config configMap: name: prometheus-config items: - key: prometheus.yml path: prometheus.yml mode: 0644 - name: prometheus-storage-volume hostPath: path: /data type: DirectoryEOF
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
kubectl apply -f prometheus-deploy.yaml
#查看prometheus是否部署成功
kubectl get pods -n monitor-sa
显示如下，可看到pod状态是running，说明prometheus部署成功
NAME READY STATUS RESTARTS AGEnode-exporter-9qpkd 1/1 Running 0 76mnode-exporter-zqmnk 1/1 Running 0 76mprometheus-server-85dbc6c7f7-nsg94 1/1 Running 0 6m7
3）给prometheus pod创建一个service
cat > prometheus-svc.yaml << EOF---apiVersion: v1kind: Servicemetadata: name: prometheus namespace: monitor-sa labels: app: prometheusspec: type: NodePort ports: - port: 9090 targetPort: 9090 protocol: TCP selector: app: prometheus component: serverEOF
#通过kubectl apply 更新service
kubectl apply -f prometheus-svc.yaml
#查看service在物理机映射的端口
kubectl get svc -n monitor-sa
显示如下：
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEprometheus NodePort 10.96.45.93 <none> 9090:31043/TCP 50s
通过上面可以看到service在宿主机上映射的端口是31043，这样我们访问k8s集群的master1节点的ip:31043，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://192.168.0.6:31043/graph
可看到如下页面：
#点击页面的Status->Targets，可看到如下,说明我们配置的服务发现可以正常采集数据
prometheus热更新
#为了每次修改配置文件可以热加载prometheus，也就是不停止prometheus，就可以使配置生效，如修改prometheus-cfg.yaml，想要使配置生效可用如下热加载命令：
curl -X POST http://10.244.1.66:9090/-/reload
#10.244.1.66是prometheus的pod的ip地址，如何查看prometheus的pod ip，可用如下命令：
kubectl get pods -n monitor-sa -o wide | grep prometheus
显示如下， 10.244.1.7就是prometheus的ip
prometheus-server-85dbc6c7f7-nsg94 1/1 Running 0 29m 10.244.1.7 node1 <none> <none>
#热加载速度比较慢，可以暴力重启prometheus，如修改上面的prometheus-cfg.yaml文件之后，可执行如下强制删除：
kubectl delete -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
然后再通过apply更新：
kubectl apply -f prometheus-cfg.yaml
kubectl apply -f prometheus-deploy.yaml
注意：
线上最好热加载，暴力删除可能造成监控数据的丢失
Grafana安装和配置
下载安装Grafana需要的镜像
上传heapster-grafana-amd64_v5_0_4.tar.gz镜像到k8s的各个master节点和k8s的各个node节点，然后在各个节点手动解压：
docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
镜像所在的百度网盘地址如下：
链接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 提取码：052u
在k8s的master1节点创建grafana.yaml
cat >grafana.yaml << EOFapiVersion: 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: NodePortEOF
通过kubectl apply 更新grafana
kubectl apply -f grafana.yaml
查看grafana是否部署成功
kubectl get pods -n kube-system
显示如下，说明部署成功
monitoring-grafana-7d7f6cf5c6-vrxw9 1/1 Running 0 3h51m
查看grafana的service
kubectl get svc -n kube-system
显示如下：
monitoring-grafana NodePort 10.111.173.47 <none> 80:31044/TCP 3h54m
上面可以看到grafana暴露的宿主机端口是31044
我们访问k8s集群的master节点ip:31044即可访问到grafana的web界面
Grafan界面接入prometheus数据源
1）登陆grafana，在浏览器访问
192.168.0.6:31044
账号密码都是admin
可看到如下界面：
2）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写 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
也可直接导入node_exporter.json监控模板，这个可以把node节点指标显示出来
node_exporter.json在百度网盘地址如下：
链接：https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 提取码：kyd6
还可直接导入docker_rev1.json，可以把容器相关的数据展示出来
docker_rev1.json在百度网盘地址如下：
链接：https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 提取码：vinv
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：
安装配置kube-state-metrics组件
kube-state-metrics是什么？
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。
安装kube-state-metrics组件
1）创建sa，并对sa授权
在k8s的master1节点生成一个kube-state-metrics-rbac.yaml文件
cat > kube-state-metrics-rbac.yaml <<EOF---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-systemEOF
通过kubectl apply更新yaml文件
kubectl apply -f kube-state-metrics-rbac.yaml
2)安装kube-state-metrics组件
在k8s的master1节点生成一个kube-state-metrics-deploy.yaml文件
cat > kube-state-metrics-deploy.yaml <<EOFapiVersion: 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: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1 image: quay.io/coreos/kube-state-metrics:v1.9.0 ports: - containerPort: 8080EOF
通过kubectl apply更新yaml文件
kubectl apply -f kube-state-metrics-deploy.yaml
查看kube-state-metrics是否部署成功
kubectl get pods -n kube-system
显示如下，看到pod处于running状态，说明部署成功
kube-state-metrics-79c9686b96-4njrs 1/1 Running 0 76s
3）创建service
在8s的master1节点生成一个kube-state-metrics-svc.yaml文件
cat >kube-state-metrics-svc.yaml <<EOFapiVersion: 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-metricsEOF
通过kubectl apply更新yaml
kubectl apply -f kube-state-metrics-svc.yaml
查看service是否创建成功
kubectl get svc -n kube-system | grep kube-state-metrics
显示如下，说明创建成功
kube-state-metrics ClusterIP 10.105.53.102 <none> 8080/TCP 2m38s
在grafana web界面导入Kubernetes Cluster (Prometheus)-1577674936972.json，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
Kubernetes Cluster (Prometheus)-1577674936972.json和Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json文件在百度网盘，地址如下：
链接：https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA 提取码：i4yd
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
cat >alertmanager-cm.yaml <<EOFkind: ConfigMapapiVersion: v1metadata: name: alertmanager namespace: monitor-sadata: alertmanager.yml: |- global: resolve_timeout: 1m smtp_smarthost: 'smtp.163.com:25' smtp_from: '15011572657@163.com' smtp_auth_username: '15011572657' smtp_auth_password: 'BDBPRMLNZGKWRFJP' smtp_require_tls: false route: group_by: [alertname] group_wait: 10s group_interval: 10s repeat_interval: 10m receiver: default-receiver receivers: - name: 'default-receiver' email_configs: - to: '1980570647@qq.com' send_resolved: trueEOF
通过kubectl apply 更新文件
kubectl apply -f alertmanager-cm.yaml
alertmanager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: '15011572657@163.com'#这是指定从哪个邮箱发送报警smtp_auth_username: '15011572657'#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: '1980570647@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
在k8s的master1节点重新生成一个prometheus-cfg.yaml文件
cat prometheus-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:$2 source_labels: - __address__ - __meta_kubernetes_pod_annotation_prometheus_io_port target_label: __address__ - action: labelmap regex: __meta_kubernetes_pod_label_(.+) - action: replace source_labels: - __meta_kubernetes_namespace target_label: kubernetes_namespace - action: replace source_labels: - __meta_kubernetes_pod_name target_label: kubernetes_pod_name - job_name: 'kubernetes-schedule' scrape_interval: 5s static_configs: - targets: ['192.168.0.6:10251'] - job_name: 'kubernetes-controller-manager' scrape_interval: 5s static_configs: - targets: ['192.168.0.6:10252'] - job_name: 'kubernetes-kube-proxy' scrape_interval: 5s static_configs: - targets: ['192.168.0.6:10249','192.168.0.56:10249'] - job_name: 'kubernetes-etcd' scheme: https tls_config: ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key scrape_interval: 5s static_configs: - targets: ['192.168.0.6:2379'] rules.yml: | groups: - name: example rules: - alert: kube-proxy的cpu使用率大于80% expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80 for: 2s labels: severity: warnning annotations: description: "{{$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：通过上面命令生成的promtheus-cfg.yaml文件会有一些问题，$1和$2这种变量在文件里没有，需要在k8s的master1节点打开promtheus-cfg.yaml文件，手动把$1和$2这种变量写进文件里，promtheus-cfg.yaml文件需要手动修改部分如下：
22行的replacement: ':9100'变成replacement: '${1}:9100'42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成 replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor73行的replacement: 变成replacement: $1:$2103行的replacement: 变成replacement: $1:$2
通过kubectl apply 更新文件
kubectl apply -f prometheus-cfg.yaml
在k8s的master1节点重新生成一个prometheus-deploy.yaml文件
cat >prometheus-deploy.yaml <<EOF---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/ShanghaiEOF
生成一个etcd-certs，这个在部署prometheus需要
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
kubectl apply -f prometheus-deploy.yaml
#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
NAME READY STATUS RESTARTS AGEprometheus-server-85dbc6c7f7-nsg94 1/1 Running 0 6m7
在k8s的master1节点重新生成一个alertmanager-svc.yaml文件
cat >alertmanager-svc.yaml <<EOF---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: NodePortEOF
通过kubectl apply更新yaml文件
kubectl apply -f prometheus-svc.yaml
#查看service在物理机映射的端口
kubectl get svc -n monitor-sa
显示如下：
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGEalertmanager NodePort 10.111.49.65 <none> 9093:31043/TCP 25sprometheus NodePort 10.96.45.93 <none> 9090:30090/TCP 34h
注意：上面可以看到prometheus的service暴漏的端口是31043，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
点击Alerts，可看到如下
把controller-manager的cpu使用率大于90%展开，可看到如下
FIRING表示prometheus已经将告警发给alertmanager，在Alertmanager 中可以看到有一个 alert。
登录到alertmanager web界面
浏览器输入192.168.0.6:30066，显示如下
这样我在我的qq邮箱，1980570647@qq.com就可以收到报警了，如下
配置Alertmanager报警-发送报警到钉钉
打开电脑版钉钉创建机器人
1.创建钉钉机器人
打开电脑版钉钉，创建一个群，创建自定义机器人，按如下步骤创建https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq我创建的机器人如下：群设置-->智能群助手-->添加机器人-->自定义-->添加机器人名称：kube-event接收群组：钉钉报警测试安全设置：自定义关键词：cluster1上面配置好之后点击完成即可，这样就会创建一个kube-event的报警机器人，创建机器人成功之后怎么查看webhook，按如下：点击智能群助手，可以看到刚才创建的kube-event这个机器人，点击kube-event，就会进入到kube-event机器人的设置界面出现如下内容：机器人名称：kube-event接受群组：钉钉报警测试消息推送：开启webhook：https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e安全设置：自定义关键词：cluster1
2.安装钉钉的webhook插件，在k8s的master1节点操作
tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz压缩包所在的百度网盘地址如下：
链接：https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ 提取码：d59o
cd prometheus-webhook-dingtalk-0.3.0.linux-amd64
启动钉钉报警插件
nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e" &
对原来的文件做备份
cp alertmanager-cm.yaml alertmanager-cm.yaml.bak
重新生成一个新的alertmanager-cm.yaml文件
cat >alertmanager-cm.yaml <<EOFkind: ConfigMapapiVersion: v1metadata: name: alertmanager namespace: monitor-sadata: alertmanager.yml: |- global: resolve_timeout: 1m smtp_smarthost: 'smtp.163.com:25' smtp_from: '15011572657@163.com' smtp_auth_username: '15011572657' smtp_auth_password: 'BDBPRMLNZGKWRFJP' smtp_require_tls: false route: group_by: [alertname] group_wait: 10s group_interval: 10s repeat_interval: 10m receiver: cluster1 receivers: - name: cluster1 webhook_configs: - url: 'http://192.168.124.16:8060/dingtalk/cluster1/send' send_resolved: trueEOF
通过kubectl apply使配置生效
kubectl delete -f alertmanager-cm.yaml
kubectl apply -f alertmanager-cm.yaml
kubectl delete -f prometheus-cfg.yaml
kubectl apply -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
kubectl apply -f prometheus-deploy.yaml
通过上面步骤，就可以实现钉钉报警了