prometheus告警

https://awesome-prometheus-alerts.grep.to/  很全的告警模板

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

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cat > kube-state-metrics-rbac.yaml <<EOF --- apiVersion: v1 kind: ServiceAccount metadata:   name: kube-state-metrics   namespace: 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.io   kind: ClusterRole   name: kube-state-metrics subjects: - kind: ServiceAccount   name: kube-state-metrics   namespace: kube-system EOF

 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

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cat > kube-state-metrics-deploy.yaml <<EOF apiVersion: apps/v1 kind: Deployment metadata:   name: kube-state-metrics   namespace: kube-system spec:   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: 8080 EOF

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

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1	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

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cat >kube-state-metrics-svc.yaml <<EOF apiVersion: v1 kind: Service metadata:   annotations:     prometheus.io/scrape: 'true'   name: kube-state-metrics   namespace: kube-system   labels:     app: kube-state-metrics spec:   ports:   - name: kube-state-metrics     port: 8080     protocol: TCP   selector:     app: kube-state-metrics EOF

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

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1	kube-state-metrics   ClusterIP   10.105.53.102    <none>        8080/TCP

 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

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cat >alertmanager-cm.yaml <<EOF kind: ConfigMap apiVersion: v1 metadata:   name: alertmanager   namespace: monitor-sa data:   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: true EOF

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

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kind: ConfigMap apiVersion: v1 metadata:   labels:     app: prometheus   name: prometheus-config   namespace: monitor-sa data:   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: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$valu

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：

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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/cadvisor 73行的replacement:  变成replacement: $1:$2 103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

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cat >prometheus-deploy.yaml <<EOF --- apiVersion: apps/v1 kind: Deployment metadata:   name: prometheus-server   namespace: monitor-sa   labels:     app: prometheus spec:   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 EOF

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

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1 2	NAME                                 READY   STATUS    RESTARTS   AGE prometheus-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

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cat >alertmanager-svc.yaml <<EOF --- apiVersion: v1 kind: Service metadata:   labels:     name: prometheus     kubernetes.io/cluster-service: 'true'   name: alertmanager   namespace: monitor-sa spec:   ports:   - name: alertmanager     nodePort: 30066     port: 9093     protocol: TCP     targetPort: 9093   selector:     app: prometheus   sessionAffinity: None   type: NodePort EOF

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：

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NAME           TYPE       CLUSTER-IP     EXTERNAL-IP   PORT(S)          AGE alertmanager   NodePort   10.111.49.65   <none>        9093:31043/TCP   25s prometheus     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

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cat >alertmanager-cm.yaml <<EOF kind: ConfigMap apiVersion: v1 metadata:   name: alertmanager   namespace: monitor-sa data:   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: true EOF

 通过kubectl apply使配置生效

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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

https://www.cnblogs.com/g2thend/articles/11865302.html   学习