文章目录
- 一、机器规划
- 二、部署安装 node-exporter、prometheus、Grafana、kube-state-metrics
- 1、创建 monitor-sa 命名空间
- 2、安装node-exporter组件
- 2.1、说明
- 2.2、应用资源清单
- 2.3、通过node-exporter采集数据
- 3、k8s 集群中部署 prometheus
- 3.1、创建一个 sa 账号
- 3.2、将 sa 账号 monitor 通过 clusterrolebing 绑定到 clusterrole 上
- 3.3、创建数据目录
- 3.4、安装prometheus
- 3.4.1、将 `prometheus.yml` 文件以 ConfigMap 的形式进行管理
- 3.4.2、应用 cm 资源清单
- 3.4.3、通过 Deployment 部署 prometheus
- 3.4.4、应用 prometheus 资源清单
- 3.4.5、给 prometheus 的 pod 创建一个 svc
- 3.4.6、应用 svc 资源清单
- 3.5、访问prometheus UI界面
- 3.6、查看配置的服务发现
- 4、prometheus热更新
- 4.1、热加载 prometheus
- 4.2、暴力重启 prometheus
- 5、Grafana安装和配置
- 5.1、下载 Grafana 需要的镜像
- 5.2、在 k8s 集群各个节点导入 Grafana 镜像
- 5.3、master 节点创建 grafana.yaml
- 5.4、查看 Grafana 的 pod 和 svc
- 5.5、查看 Grafana UI 界面
- 5.6、给 Grafana 接入 Prometheus 数据源
- 5.7、获取监控模板
- 5.8、导入监控模板
- 6、安装配置 kube-state-metrics 组件
- 6.1、什么是 kube-state-metrics
- 6.2、创建 sa ，并进行授权
- 6.3、创建并应用 kube-state-metrics-deploy.yaml 文件
- 6.4、创建并应用 kube-state-metrics-svc.yaml 文件
- 6.5、获取 kube-state-metrics json 文件
- 6.6、向 Grafana 导入 kube-state-metrics json 文件
- 三、安装和配置 Alertmanager -- 发送告警到 QQ 邮箱
- 1、将 alertmanager-cm.yaml 文件以 cm 形式进行管理
- 1.1、alertmanager配置文件说明
- 2、重新生成并应用 prometheus-cfg.yaml 文件
- 3、重新生成 prometheus-deploy.yaml 文件
- 3.1、创建一个名为 etcd-certs 的 Secret
- 3.2、应用 prometheus-deploy.yaml 文件
- 4、重新生成并创建 alertmanager-svc.yaml 文件
- 5、访问 prometheus UI 界面
- 5.1、【error】kube-controller-manager、etcd、kube-proxy、kube-scheduler 组件 connection refused
- 5.1.1、kube-proxy
- 5.1.2、kube-controller-manager
- 5.1.3、kube-schedule
- 5.1.4、etcd
- 6、点击Alerts，查看
- 7、把controller-manager的cpu使用率大于90%展开
- 8、登录 alertmanager UI
- 9、登录 QQ 邮箱查看告警信息
- 四、配置 Alertmanager 报警 -- 发送告警到钉钉
- 1、手机端拉群
- 2、创建自定义机器人
- 3、获取钉钉的 Webhook 插件
- 4、启动钉钉告警插件
- 5、对 alertmanager-cm.yaml 文件做备份
- 6、重新生成新的 alertmanager-cm.yaml 文件
- 7、重建资源以生效
- 8、效果
一、机器规划
| 角色 | 主机名 | ip 地址 |
|---|---|---|
| master | k8s-master1 | 192.168.112.10 |
| node | k8s-node1 | 192.168.112.20 |
| node | k8s-node2 | 192.168.112.30 |
| 平台 | VMware Workstation |
|---|---|
| 操作系统 | CentOS Linux release 7.9.2009 (Core) |
| 内存、CPU | 4C4G |
| 磁盘大小 | 20G SCSI |
二、部署安装 node-exporter、prometheus、Grafana、kube-state-metrics
1、创建 monitor-sa 命名空间
master 节点操作
kubectl create ns monitor-sa
2、安装node-exporter组件
master 节点操作
cat >> node-export.yaml <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:name: node-exporternamespace: monitor-salabels:name: node-exporter
spec:selector:matchLabels:name: node-exportertemplate:metadata:labels:name: node-exporterspec:hostPID: truehostIPC: truehostNetwork: truecontainers:- name: node-exporterimage: prom/node-exporter:v0.16.0ports:- containerPort: 9100resources:requests:cpu: 0.15securityContext:privileged: trueargs:- --path.procfs- /host/proc- --path.sysfs- /host/sys- --collector.filesystem.ignored-mount-points- '"^/(sys|proc|dev|host|etc)($|/)"'volumeMounts:- name: devmountPath: /host/dev- name: procmountPath: /host/proc- name: sysmountPath: /host/sys- name: rootfsmountPath: /rootfstolerations:- key: "node-role.kubernetes.io/master"operator: "Exists"effect: "NoSchedule"volumes:- name: prochostPath:path: /proc- name: devhostPath:path: /dev- name: syshostPath:path: /sys- name: rootfshostPath:path: /
EOF
2.1、说明
- 主机命名空间共享 (
hostPID,hostIPC,hostNetwork)hostPID: true: 允许 Pod 使用主机的 PID 命名空间。Pod 可以看到主机上的所有进程hostIPC: true: 允许 Pod 使用主机的 IPC 命名空间。Pod 可以与其他在主机上运行的进程共享 IPC 资源（如信号量、消息队列等）。hostNetwork: true: 允许 Pod 使用主机的网络命名空间。Pod 将使用主机的网络接口
- 命令行参数 (
args) --path.procfs /host/proc: 指定node-exporter应该从/host/proc路径读取进程文件系统的数据。这使得node-exporter可以访问宿主机的进程信息。--path.sysfs /host/sys: 指定node-exporter应该从/host/sys路径读取系统文件系统的数据。这使得node-exporter可以访问宿主机的系统信息。--collector.filesystem.ignored-mount-points "^/(sys|proc|dev|host|etc)($|/)": 指定哪些文件系统的挂载点应该被忽略，不被node-exporter收集。这里忽略了/sys,/proc,/dev,/host, 和/etc这些挂载点，避免收集不必要的数据。- 挂载点 (
volumeMounts和volumes)/proc挂载- 宿主机路径:
/proc - 容器内路径:
/host/proc - 作用: 让
node-exporter访问宿主机的进程文件系统。
- 宿主机路径:
/dev挂载- 宿主机路径:
/dev - 容器内路径:
/host/dev - 作用: 让
node-exporter访问宿主机的设备文件。
- 宿主机路径:
/sys挂载- 宿主机路径:
/sys - 容器内路径:
/host/sys - 作用: 让
node-exporter访问宿主机的系统文件系统。
- 宿主机路径:
/挂载- 宿主机路径:
/ - 容器内路径:
/rootfs - 作用: 让
node-exporter访问宿主机的根文件系统。
- 宿主机路径:
- 容忍度 (
tolerations)key: "node-role.kubernetes.io/master": 指定容忍的污点键。operator: "Exists": 表示只要存在该污点键，无论值是什么，都予以容忍。effect: "NoSchedule": 表示即使节点上有这种污点，也不会阻止 Pod 被调度到该节点上。
2.2、应用资源清单
kubectl apply -f node-export.yamlkubectl get pods -n monitor-sa -l name=node-exporter
2.3、通过node-exporter采集数据
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://<master-ip>:9100/metricscurl http://192.168.112.10:9100/metrics
3、k8s 集群中部署 prometheus
3.1、创建一个 sa 账号
kubectl create serviceaccount monitor -n monitor-sa
3.2、将 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
3.3、创建数据目录
所有 node 节点
mkdir /data && chmod 777 /data/
3.4、安装prometheus
master 节点操作
3.4.1、将 prometheus.yml 文件以 ConfigMap 的形式进行管理
cat >> prometheus-cfg.yaml << 'EOF'
---
kind: ConfigMap
apiVersion: v1
metadata:labels:app: prometheusname: prometheus-confignamespace: monitor-sa
data:prometheus.yml: |global:scrape_interval: 15sscrape_timeout: 10sevaluation_interval: 1mscrape_configs:- job_name: 'kubernetes-node'kubernetes_sd_configs:- role: noderelabel_configs:- source_labels: [__address__]regex: '(.*):10250'replacement: '${1}:9100'target_label: __address__action: replace- action: labelmapregex: __meta_kubernetes_node_label_(.+)- job_name: 'kubernetes-node-cadvisor'kubernetes_sd_configs:- role: nodescheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crtbearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/tokenrelabel_configs:- action: labelmapregex: __meta_kubernetes_node_label_(.+)- target_label: __address__replacement: kubernetes.default.svc:443- source_labels: [__meta_kubernetes_node_name]regex: (.+)target_label: __metrics_path__replacement: '/api/v1/nodes/${1}/proxy/metrics/cadvisor'- job_name: 'kubernetes-apiserver'kubernetes_sd_configs:- role: endpointsscheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crtbearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/tokenrelabel_configs:- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]action: keepregex: default;kubernetes;https- job_name: 'kubernetes-service-endpoints'kubernetes_sd_configs:- role: endpointsrelabel_configs:- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]action: keepregex: true- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]action: replacetarget_label: __scheme__regex: (https?)- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]action: replacetarget_label: __metrics_path__regex: (.+)- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]action: replacetarget_label: __address__regex: ([^:]+)(?::\d+)?;(\d+)replacement: '$1:$2'- action: labelmapregex: __meta_kubernetes_service_label_(.+)- source_labels: [__meta_kubernetes_namespace]action: replacetarget_label: kubernetes_namespace- source_labels: [__meta_kubernetes_service_name]action: replacetarget_label: kubernetes_name
EOF
3.4.2、应用 cm 资源清单
kubectl apply -f prometheus-cfg.yamlkubectl get cm prometheus-config -n monitor-sa -o yaml
需要确保 cm 正确解析了变量 $1、$2
不然 prometheus 获取不到对应的 IP 地址会无法正常监控
3.4.3、通过 Deployment 部署 prometheus
cat >> prometheus-deploy.yaml << EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:name: prometheus-servernamespace: monitor-salabels:app: prometheus
spec:replicas: 2selector:matchLabels:app: prometheuscomponent: server#matchExpressions:#- {key: app, operator: In, values: [prometheus]}#- {key: component, operator: In, values: [server]}template:metadata:labels:app: prometheuscomponent: serverannotations:prometheus.io/scrape: 'false'spec:affinity:podAntiAffinity:requiredDuringSchedulingIgnoredDuringExecution:- labelSelector:matchExpressions:- key: appoperator: Invalues:- prometheus- key: componentoperator: Invalues:- servertopologyKey: kubernetes.io/hostnameserviceAccountName: monitorcontainers:- name: prometheusimage: quay.io/prometheus/prometheus:latestimagePullPolicy: IfNotPresentcommand:- prometheus- --config.file=/etc/prometheus/prometheus.yml- --storage.tsdb.path=/prometheus- --storage.tsdb.retention=720hports:- containerPort: 9090protocol: TCPvolumeMounts:- mountPath: /etc/prometheus/prometheus.ymlname: prometheus-configsubPath: prometheus.yml- mountPath: /prometheus/name: prometheus-storage-volumevolumes:- name: prometheus-configconfigMap:name: prometheus-configitems:- key: prometheus.ymlpath: prometheus.ymlmode: 0644- name: prometheus-storage-volumehostPath:path: /datatype: Directory
EOF
3.4.4、应用 prometheus 资源清单
kubectl apply -f prometheus-deploy.yaml
3.4.5、给 prometheus 的 pod 创建一个 svc
cat > prometheus-svc.yaml << EOF
---
apiVersion: v1
kind: Service
metadata:name: prometheusnamespace: monitor-salabels:app: prometheus
spec:type: NodePortports:- port: 9090targetPort: 9090protocol: TCPselector:app: prometheuscomponent: server
EOF
3.4.6、应用 svc 资源清单
kubectl get svc -n monitor-sa -o wide
通过上面可以看到service在宿主机上映射的端口是30172，这样我们访问k8s集群的k8s-master1节点的ip:30172，就可以访问到prometheus的web ui界面了
3.5、访问prometheus UI界面
# <k8s-master1 IP>:32032
192.168.112.10:32032
3.6、查看配置的服务发现
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
4、prometheus热更新
4.1、热加载 prometheus
#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://<prometheus-pod-ip>:9090/-/reload
kubectl get pods -n monitor-sa -l app=prometheus -o wide
4.2、暴力重启 prometheus
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.yamlkubectl delete -f prometheus-deploy.yaml# 然后再通过apply更新kubectl apply -f prometheus-cfg.yamlkubectl apply -f prometheus-deploy.yaml
线上最好热加载，暴力删除可能造成监控数据的丢失
5、Grafana安装和配置
5.1、下载 Grafana 需要的镜像
链接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA
提取码：052u
5.2、在 k8s 集群各个节点导入 Grafana 镜像
docker load -i heapster-grafana-amd64_v5_0_4.tar.gzdocker images | grep grafana
5.3、master 节点创建 grafana.yaml
cat >> grafana.yaml << EOF
apiVersion: apps/v1
kind: Deployment
metadata:name: monitoring-grafananamespace: kube-system
spec:replicas: 1selector:matchLabels:task: monitoringk8s-app: grafanatemplate:metadata:labels:task: monitoringk8s-app: grafanaspec:containers:- name: grafanaimage: k8s.gcr.io/heapster-grafana-amd64:v5.0.4ports:- containerPort: 3000protocol: TCPvolumeMounts:- mountPath: /etc/ssl/certsname: ca-certificatesreadOnly: true- mountPath: /varname: grafana-storageenv:- name: INFLUXDB_HOSTvalue: monitoring-influxdb- name: GF_SERVER_HTTP_PORTvalue: "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_ENABLEDvalue: "false"- name: GF_AUTH_ANONYMOUS_ENABLEDvalue: "true"- name: GF_AUTH_ANONYMOUS_ORG_ROLEvalue: 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/proxyvalue: /volumes:- name: ca-certificateshostPath:path: /etc/ssl/certs- name: grafana-storageemptyDir: {}
---
apiVersion: v1
kind: Service
metadata: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-grafananame: monitoring-grafananamespace: kube-system
spec:# 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: NodePortports:- port: 80targetPort: 3000selector:k8s-app: grafanatype: NodePort
EOF
5.4、查看 Grafana 的 pod 和 svc
5.5、查看 Grafana UI 界面
# <master-ip>:<grafana-svc-port>192.168.112.10:31455
5.6、给 Grafana 接入 Prometheus 数据源
| 选择 Create your first data source |
|---|
 |
 |
| Name: Prometheus |Type: Prometheus|HTTP 处的URL写 如下：http://prometheus.monitor-sa.svc:9090 |
 |
| 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 |
 |
 |
5.7、获取监控模板
- 可以在 Grafana Dashboard 官网搜索需要的
Grafana dashboards | Grafana Labs
- 也可以直接克隆 Github 仓库，获取 node_exporter.json 、 docker_rev1.json 监控模板
git clone git@github.com:misakivv/Grafana-Dashboard.git
5.8、导入监控模板
| 依次点击左侧栏的 + 号下方的 Import |
|---|
 |
| 选择 Upload json file，选择一个本地的node_exporter.json 文件 |
 |
| 导入后 Options 选项中会出现 Name 是自动生成的，Prometheus 是需要我们选择 Prometheus的 |
 |
| 点击 Import 即可出现如下界面 |
 |
| 按照如上操作，导入docker_rev1.json监控模板 |
 |
 |
6、安装配置 kube-state-metrics 组件
6.1、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？有多少job在运行中。
6.2、创建 sa ，并进行授权
k8s-master1 节点编写一个 kube-state-metrics-rbac.yaml 文件
cat >> kube-state-metrics-rbac.yaml << EOF
---
apiVersion: v1
kind: ServiceAccount
metadata:name: kube-state-metricsnamespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:name: kube-state-metrics
rules:
- 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/v1
kind: ClusterRoleBinding
metadata:name: kube-state-metrics
roleRef:apiGroup: rbac.authorization.k8s.iokind: ClusterRolename: kube-state-metrics
subjects:
- kind: ServiceAccountname: kube-state-metricsnamespace: kube-system
EOF
kubectl get sa,clusterrole,clusterrolebinding -n kube-system | grep kube-state-metrics
6.3、创建并应用 kube-state-metrics-deploy.yaml 文件
k8s-master1 节点操作
cat > kube-state-metrics-deploy.yaml <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:name: kube-state-metricsnamespace: kube-system
spec:replicas: 1selector:matchLabels:app: kube-state-metricstemplate:metadata:labels:app: kube-state-metricsspec:serviceAccountName: kube-state-metricscontainers:- name: kube-state-metrics
# image: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1image: quay.io/coreos/kube-state-metrics:latestports:- containerPort: 8080
EOF
kubectl apply -f kube-state-metrics-deploy.yamlkubectl get pods -n kube-system -l app=kube-state-metrics -w
拉取 kube-state-metrics 指定镜像版本失败时可以选择在集群各个节点上
docker pull quay.io/coreos/kube-state-metrics:latest
拉取最新 tag 版本
6.4、创建并应用 kube-state-metrics-svc.yaml 文件
k8s-master1 节点操作
cat >> kube-state-metrics-svc.yaml <<EOF
apiVersion: v1
kind: Service
metadata:annotations:prometheus.io/scrape: 'true'name: kube-state-metricsnamespace: kube-systemlabels:app: kube-state-metrics
spec:ports:- name: kube-state-metricsport: 8080protocol: TCPselector:app: kube-state-metrics
EOF
kubectl apply -f kube-state-metrics-svc.yamlkubectl get svc -n kube-system -l app=kube-state-metrics
6.5、获取 kube-state-metrics json 文件
git clone git@github.com:misakivv/Grafana-Dashboard.git
6.6、向 Grafana 导入 kube-state-metrics json 文件
| 点击左侧栏 + 号的 Import |
|---|
 |
| 点击 Upload .json File，上传 Kubernetes Cluster (Prometheus)-1577674936972.json |
 |
 |
| 查看 |
 |
| **同样的导入 Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json ** |
 |
 |
 |
 |
 |
 |
 |
 |
 |
三、安装和配置 Alertmanager – 发送告警到 QQ 邮箱
1、将 alertmanager-cm.yaml 文件以 cm 形式进行管理
k8s-master1 节点操作
cat >> alertmanager-cm.yaml << EOF
kind: ConfigMap
apiVersion: v1
metadata:name: alertmanagernamespace: monitor-sa
data:alertmanager.yml: |-global:resolve_timeout: 1msmtp_smarthost: 'smtp.qq.com:465'smtp_from: '2830909671@qq.com'smtp_auth_username: '2830909671@qq.com'smtp_auth_password: 'ajjgpgwwfkpcdgih'smtp_require_tls: falseroute:group_by: [alertname]group_wait: 5sgroup_interval: 5srepeat_interval: 5mreceiver: default-receiverreceivers:- name: 'default-receiver'email_configs:- to: 'misakikk@qq.com'send_resolved: true
EOF
kubectl apply -f alertmanager-cm.yamlkubectl get cm alertmanager -n monitor-sa
1.1、alertmanager配置文件说明
smtp_smarthost: 'smtp.qq.com:465'
#用于发送邮件的邮箱的SMTP服务器地址+端口。QQ 邮箱 SMTP 服务地址，官方地址为 smtp.qq.com 端口为 465 或 587，同时要设置开启 POP3/SMTP 服务。
smtp_from: '2830909671@qq.com'
#这是指定从哪个邮箱发送报警
smtp_auth_password: 'ajjgpgwwfkpcdgih'
#这是发送邮箱的授权码而不是登录密码
email_configs:- to: 'misakikk@qq.com'
#to后面指定发送到哪个邮箱
2、重新生成并应用 prometheus-cfg.yaml 文件
k8s-master1 节点操作
cat > prometheus-cfg.yaml << 'EOF'
kind: ConfigMap
apiVersion: v1
metadata:labels:app: prometheusname: prometheus-confignamespace: monitor-sa
data:prometheus.yml: |rule_files:- /etc/prometheus/rules.ymlalerting:alertmanagers:- static_configs:- targets: ["localhost:9093"]global:scrape_interval: 15sscrape_timeout: 10sevaluation_interval: 1mscrape_configs:- job_name: 'kubernetes-node'kubernetes_sd_configs:- role: noderelabel_configs:- source_labels: [__address__]regex: '(.*):10250'replacement: '${1}:9100'target_label: __address__action: replace- action: labelmapregex: __meta_kubernetes_node_label_(.+)- job_name: 'kubernetes-node-cadvisor'kubernetes_sd_configs:- role: nodescheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crtbearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/tokenrelabel_configs:- action: labelmapregex: __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: endpointsscheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crtbearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/tokenrelabel_configs:- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]action: keepregex: default;kubernetes;https- job_name: 'kubernetes-service-endpoints'kubernetes_sd_configs:- role: endpointsrelabel_configs:- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]action: keepregex: true- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]action: replacetarget_label: __scheme__regex: (https?)- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]action: replacetarget_label: __metrics_path__regex: (.+)- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]action: replacetarget_label: __address__regex: ([^:]+)(?::\d+)?;(\d+)replacement: '$1:$2'- action: labelmapregex: __meta_kubernetes_service_label_(.+)- source_labels: [__meta_kubernetes_namespace]action: replacetarget_label: kubernetes_namespace- source_labels: [__meta_kubernetes_service_name]action: replacetarget_label: kubernetes_name - job_name: 'kubernetes-pods'kubernetes_sd_configs:- role: podrelabel_configs:- action: keepregex: truesource_labels:- __meta_kubernetes_pod_annotation_prometheus_io_scrape- action: replaceregex: (.+)source_labels:- __meta_kubernetes_pod_annotation_prometheus_io_pathtarget_label: __metrics_path__- action: replaceregex: ([^:]+)(?::\d+)?;(\d+)replacement: '$1:$2'source_labels:- __address__- __meta_kubernetes_pod_annotation_prometheus_io_porttarget_label: __address__- action: labelmapregex: __meta_kubernetes_pod_label_(.+)- action: replacesource_labels:- __meta_kubernetes_namespacetarget_label: kubernetes_namespace- action: replacesource_labels:- __meta_kubernetes_pod_nametarget_label: kubernetes_pod_name- job_name: 'kubernetes-schedule'scrape_interval: 5sstatic_configs:- targets: ['192.168.112.10:10259']- job_name: 'kubernetes-controller-manager'scrape_interval: 5sstatic_configs:- targets: ['192.168.112.10:10257']- job_name: 'kubernetes-kube-proxy'scrape_interval: 5sstatic_configs:- targets: ['192.168.112.10:10249','192.168.112.20:10249','192.168.112.30:10249']- job_name: 'kubernetes-etcd'scheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crtcert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crtkey_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.keyscrape_interval: 5sstatic_configs:- targets: ['192.168.112.10:2381']rules.yml: |groups:- name: examplerules:- alert: kube-proxy的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"- alert: kube-proxy的cpu使用率大于90%expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"- alert: scheduler的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"- alert: scheduler的cpu使用率大于90%expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"- alert: controller-manager的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"- alert: controller-manager的cpu使用率大于90%expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"- alert: apiserver的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"- alert: apiserver的cpu使用率大于90%expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"- alert: etcd的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"- alert: etcd的cpu使用率大于90%expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"- alert: kube-state-metrics的cpu使用率大于80%expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"value: "{{ $value }}%"threshold: "80%" - alert: kube-state-metrics的cpu使用率大于90%expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"value: "{{ $value }}%"threshold: "90%" - alert: coredns的cpu使用率大于80%expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"value: "{{ $value }}%"threshold: "80%" - alert: coredns的cpu使用率大于90%expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"value: "{{ $value }}%"threshold: "90%" - alert: kube-proxy打开句柄数>600expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 600for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"value: "{{ $value }}"- alert: kube-proxy打开句柄数>1000expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 1000for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"value: "{{ $value }}"- alert: kubernetes-schedule打开句柄数>600expr: process_open_fds{job=~"kubernetes-schedule"} > 600for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"value: "{{ $value }}"- alert: kubernetes-schedule打开句柄数>1000expr: process_open_fds{job=~"kubernetes-schedule"} > 1000for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"value: "{{ $value }}"- alert: kubernetes-controller-manager打开句柄数>600expr: process_open_fds{job=~"kubernetes-controller-manager"} > 600for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"value: "{{ $value }}"- alert: kubernetes-controller-manager打开句柄数>1000expr: process_open_fds{job=~"kubernetes-controller-manager"} > 1000for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"value: "{{ $value }}"- alert: kubernetes-apiserver打开句柄数>600expr: process_open_fds{job=~"kubernetes-apiserver"} > 600for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"value: "{{ $value }}"- alert: kubernetes-apiserver打开句柄数>1000expr: process_open_fds{job=~"kubernetes-apiserver"} > 1000for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"value: "{{ $value }}"- alert: kubernetes-etcd打开句柄数>600expr: process_open_fds{job=~"kubernetes-etcd"} > 600for: 2slabels:severity: warnningannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"value: "{{ $value }}"- alert: kubernetes-etcd打开句柄数>1000expr: process_open_fds{job=~"kubernetes-etcd"} > 1000for: 2slabels:severity: criticalannotations:description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"value: "{{ $value }}"- alert: corednsexpr: process_open_fds{k8s_app=~"kube-dns"} > 600for: 2slabels:severity: warnning annotations:description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"value: "{{ $value }}"- alert: corednsexpr: process_open_fds{k8s_app=~"kube-dns"} > 1000for: 2slabels:severity: criticalannotations:description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"value: "{{ $value }}"- alert: kube-proxyexpr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"} > 2000000000for: 2slabels:severity: warnningannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: schedulerexpr: process_virtual_memory_bytes{job=~"kubernetes-schedule"} > 2000000000for: 2slabels:severity: warnningannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: kubernetes-controller-managerexpr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"} > 2000000000for: 2slabels:severity: warnningannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: kubernetes-apiserverexpr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"} > 2000000000for: 2slabels:severity: warnningannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: kubernetes-etcdexpr: process_virtual_memory_bytes{job=~"kubernetes-etcd"} > 2000000000for: 2slabels:severity: warnningannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: kube-dnsexpr: process_virtual_memory_bytes{k8s_app=~"kube-dns"} > 2000000000for: 2slabels:severity: warnningannotations:description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"value: "{{ $value }}"- alert: HttpRequestsAvgexpr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m])) > 1000for: 2slabels:team: adminannotations:description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"value: "{{ $value }}"threshold: "1000" - alert: Pod_restartsexpr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0for: 2slabels:severity: warnningannotations:description: "在{{$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: "{{ $value }}"threshold: "0"- alert: Pod_waitingexpr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1for: 2slabels:team: adminannotations:description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"value: "{{ $value }}"threshold: "1" - alert: Pod_terminatedexpr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1for: 2slabels:team: adminannotations:description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"value: "{{ $value }}"threshold: "1"- alert: Etcd_leaderexpr: etcd_server_has_leader{job="kubernetes-etcd"} == 0for: 2slabels:team: adminannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"value: "{{ $value }}"threshold: "0"- alert: Etcd_leader_changesexpr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0for: 2slabels:team: adminannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"value: "{{ $value }}"threshold: "0"- alert: Etcd_failedexpr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0for: 2slabels:team: adminannotations:description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"value: "{{ $value }}"threshold: "0"- alert: Etcd_db_total_sizeexpr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000for: 2slabels:team: adminannotations:description: "组件{{$labels.job}}({{$labels.instance}})：db空间超过10G"value: "{{ $value }}"threshold: "10G"- alert: Endpoint_readyexpr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1for: 2slabels:team: adminannotations:description: "空间{{$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 > 90for: 2slabels:severity: ccriticalannotations: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 > 90for: 2slabels:severity: criticalannotations: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: InstanceDownexpr: up == 0for: 2slabels:severity: criticalannotations: summary: "{{ $labels.instance }}: 服务器宕机"description: "{{ $labels.instance }}: 服务器延时超过2分钟"- alert: 物理节点磁盘的IO性能expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60for: 2slabels:severity: criticalannotations:summary: "{{$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) > 102400for: 2slabels:severity: criticalannotations: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{{$value}}"- alert: 出网流量带宽expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400for: 2slabels:severity: criticalannotations:summary: "{{$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 > 1000for: 2slabels:severity: criticalannotations:summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高！"description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"- alert: 磁盘容量expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80for: 2slabels:severity: criticalannotations:summary: "{{$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}}%)"
EOF
注意：
除了
kube-proxy默认在每个节点的10249端口上暴露其指标其余的
kubernetes-schedule、kubernetes-controller-manager、kubernetes-etcd这些组件Pod 的容器需要根据自己的 k8s 集群情况进行修改
kubectl apply -f prometheus-cfg.yamlkubectl get cm prometheus-config -n monitor-sa -o yaml
同样的还是需要检查 cm 文件中是否正确解析了 $1 $2
3、重新生成 prometheus-deploy.yaml 文件
k8s-master1 节点操作
cat > prometheus-deploy.yaml << EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:name: prometheus-servernamespace: monitor-salabels:app: prometheus
spec:replicas: 2selector:matchLabels:app: prometheuscomponent: server#matchExpressions:#- {key: app, operator: In, values: [prometheus]}#- {key: component, operator: In, values: [server]}template:metadata:labels:app: prometheuscomponent: serverannotations:prometheus.io/scrape: 'false'spec:affinity:podAntiAffinity:requiredDuringSchedulingIgnoredDuringExecution:- labelSelector:matchExpressions:- key: appoperator: Invalues:- prometheus- key: componentoperator: Invalues:- servertopologyKey: kubernetes.io/hostnameserviceAccountName: monitorcontainers:- name: prometheusimage: quay.io/prometheus/prometheus:latestimagePullPolicy: IfNotPresentcommand:- "/bin/prometheus"args:- "--config.file=/etc/prometheus/prometheus.yml"- "--storage.tsdb.path=/prometheus"- "--storage.tsdb.retention=24h"- "--web.enable-lifecycle"ports:- containerPort: 9090protocol: TCPvolumeMounts:- mountPath: /etc/prometheusname: prometheus-config- mountPath: /prometheus/name: prometheus-storage-volume- name: k8s-certsmountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/- name: alertmanagerimage: prom/alertmanager:latestimagePullPolicy: IfNotPresentargs:- "--config.file=/etc/alertmanager/alertmanager.yml"- "--log.level=debug"ports:- containerPort: 9093protocol: TCPname: alertmanagervolumeMounts:- name: alertmanager-configmountPath: /etc/alertmanager- name: alertmanager-storagemountPath: /alertmanager- name: localtimemountPath: /etc/localtimevolumes:- name: prometheus-configconfigMap:name: prometheus-config- name: prometheus-storage-volumehostPath:path: /datatype: Directory- name: k8s-certssecret:secretName: etcd-certs- name: alertmanager-configconfigMap:name: alertmanager- name: alertmanager-storagehostPath:path: /data/alertmanagertype: DirectoryOrCreate- name: localtimehostPath:path: /usr/share/zoneinfo/Asia/Shanghai
EOF
3.1、创建一个名为 etcd-certs 的 Secret
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
3.2、应用 prometheus-deploy.yaml 文件
kubectl apply -f prometheus-deploy.yamlkubectl get pods -n monitor-sa
4、重新生成并创建 alertmanager-svc.yaml 文件
cat >alertmanager-svc.yaml <<EOF
---
apiVersion: v1
kind: Service
metadata:labels:name: prometheuskubernetes.io/cluster-service: 'true'name: alertmanagernamespace: monitor-sa
spec:ports:- name: alertmanagernodePort: 30066port: 9093protocol: TCPtargetPort: 9093selector:app: prometheussessionAffinity: Nonetype: NodePort
EOF
kubectl apply -f alertmanager-svc.yamlkubectl get svc alertmanager -n monitor-sa
5、访问 prometheus UI 界面
5.1、【error】kube-controller-manager、etcd、kube-proxy、kube-scheduler 组件 connection refused
5.1.1、kube-proxy
默认情况下，该服务监听端口只提供给127.0.0.1，需修改为0.0.0.0
kubectl edit cm/kube-proxy -n kube-system
- 编辑文件，将文件修改允许0.0.0.0即可，保存
metricsBindAddress: 0.0.0.0:10249
- 删除重建 kube-proxy 的 pod
kubectl delete pod -l k8s-app=kube-proxy -n kube-system
- 效果
5.1.2、kube-controller-manager
事先说明：到这一步我试过网上很多方法都没有成功获取到数据，所以我重新创建了 sa 慎用，仅供参考
- 修改 kube-controller-manager 的 yaml 文件
默认监听本地修改为 0.0.0.0
- --bind-address=127.0.0.1
# 修改为
- --bind-address=0.0.0.0
- 创建ServiceAccount
创建一个新的ServiceAccount，用于Prometheus访问 kube-controller-manager。
cat > prom-sa << EOF
apiVersion: v1
kind: ServiceAccount
metadata:name: prometheus-sanamespace: monitor-sa
EOF
- 创建ClusterRole
创建一个ClusterRole，定义Prometheus所需的权限。
cat > porm-role << EOF
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:name: prometheus-role
rules:
- nonResourceURLs:- "/metrics"verbs:- get
EOF
- 创建ClusterRoleBinding
将ServiceAccount绑定到ClusterRole。
cat > prom-bind.yaml << EOF
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:name: prometheus-binding
subjects:
- kind: ServiceAccountname: prometheus-sanamespace: monitor-sa
roleRef:kind: ClusterRolename: prometheus-roleapiGroup: rbac.authorization.k8s.io
EOF
- 获取ServiceAccount的Token
获取ServiceAccount的Token，以便在Prometheus配置中使用。
TOKEN=$(kubectl get secret $(kubectl get sa prometheus-sa -n monitor-sa -o json | jq -r '.secrets[].name') -n monitor-sa -o json | jq -r '.data.token' | base64 --decode)
- 修改Prometheus配置文件（cm）
- job_name: 'kubernetes-controller-manager'scheme: httpstls_config:insecure_skip_verify: true # 禁用证书验证authorization:credentials: eyJhbGciOiJSUzI1NiIsImtpZCI6IkFEWVNqaWlueWVDMzBUcTZvQk9MRkpxQ0diLWRGWkNoaWlpZkgwR21NcEkifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJtb25pdG9yLXNhIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZWNyZXQubmFtZSI6InByb21ldGhldXMtc2EtdG9rZW4tbnQ5bm4iLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoicHJvbWV0aGV1cy1zYSIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6IjQ4YTA5NDExLTAwMmYtNDE0Ni05YzY4LTBiNmVjOWYzYWZlZCIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDptb25pdG9yLXNhOnByb21ldGhldXMtc2EifQ.DNgCjTVxsrGDltvQZG-x7qPQrh369SO_e0faGrrhjgkBLS4q2sh85wkaBNNZcIjxZcVk7ZU9gQmQkM3AIgGIcIURpQGDMgVVI_xF1JV8iQWe-nL1yHnQAXDjyMAd1826wVvMH8LSKqdKfPVaMHN8t0LScX5yHonSJUqoevxi7Mm7tiUd33IlMQ6xH6M8Tu8bsg-fOVmL6nnGpC1tPgaZy8M_GA_Kh9j8SwHXi4Yd9r75eOSa3J6N4KF6n-EPKxnGmXDooA60G94YptsDFCQMi1t4TLAFR1FKraycWHwPbIwviUZTvA1WXbkiHnh0R6q-y0hHJVbAi_ZXagVXKZFBaw # 替换为实际的Token值scrape_interval: 5sstatic_configs:- targets: ['192.168.112.10:10257']
- 重启Prometheus
更新配置后，重启Prometheus以应用新的配置。
kubectl rollout restart deployment/prometheus-server -n monitor-sa
- 效果
5.1.3、kube-schedule
和 kube-controller-manager 操作一致
- 效果
5.1.4、etcd
- 修改创建 etcd 的 yaml 文件
添加 master 节点 ip + etcd port
vim /etc/kubernetes/manifests/etcd.yaml- --listen-metrics-urls=http://127.0.0.1:2381,http://192.168.112.10:2381
- 修改 prometheus.yaml 文件
改为 http
- 效果
6、点击Alerts，查看
7、把controller-manager的cpu使用率大于90%展开
FIRING表示prometheus已经将告警发给alertmanager
在Alertmanager 中可以看到有 alert。
8、登录 alertmanager UI
<master-ip>:svc-alertmanager-port192.168.112.10:30066
9、登录 QQ 邮箱查看告警信息
四、配置 Alertmanager 报警 – 发送告警到钉钉
1、手机端拉群
因为 PC 端不好操作
2、创建自定义机器人
自定义机器人安全设置 - 钉钉开放平台 (dingtalk.com)
| 群设置 |
|---|
 |
| 机器人 |
 |
| 添加机器人 |
 |
| 自定义 |
 |
| 添加 |
 |
| 机器人名字、安全设置 |
 |
| 保管好 Webhook |
 |
3、获取钉钉的 Webhook 插件
master 节点操作
git clone git@github.com:misakivv/prometheus-webhook-dingtalk.gitcd prometheus-webhook-dingtalktar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gzcd prometheus-webhook-dingtalk-0.3.0.linux-amd64
4、启动钉钉告警插件
nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=feb3df2c6a987c8c1466c16eb90f4c2d3817c481aacf15cecc46f588f2716f25" &
5、对 alertmanager-cm.yaml 文件做备份
cp alertmanager-cm.yaml alertmanager-cm.yaml.bak
6、重新生成新的 alertmanager-cm.yaml 文件
cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:name: alertmanagernamespace: monitor-sa
data:alertmanager.yml: |-global:resolve_timeout: 1msmtp_smarthost: 'smtp.qq.com:465'smtp_from: '2830909671@qq.com'smtp_auth_username: '2830909671@qq.com'smtp_auth_password: 'ajjgpgwwfkpcdgih'smtp_require_tls: falseroute:group_by: [alertname]group_wait: 10sgroup_interval: 10srepeat_interval: 10mreceiver: cluster1receivers:- name: cluster1webhook_configs:- url: 'http://192.168.112.10:8060/dingtalk/cluster1/send'send_resolved: true
EOF
7、重建资源以生效
kubectl delete cm alertmanager -n monitor-sakubectl apply -f alertmanager-cm.yamlkubectl delete -f prometheus-cfg.yamlkubectl apply -f prometheus-cfg.yamlkubectl delete -f prometheus-deploy.yamlkubectl apply -f prometheus-deploy.yaml
8、效果
 |
|---|
 |
 |
 |
 |
 |
暂时先写到这里，其实 alertmanager 还有静默、去重、抑制等功能，告警也可以模板化，下一篇再共同学习
