k8s—Prometheus+Grafana+Altermaneger构建监控平台
目录
一、安装node-exporter
1.下载所需镜像
2.编写node-export.yaml文件并应用
3.测试node-exporter并获取数据
二、Prometheus server安装和配置
1.创建sa(serviceaccount)账号，对sa做rabc授权
1）创建一个 sa 账号 monitor
2）把 sa 账号 monitor 通过 clusterrolebing 绑定到 clusterrole 上
2.创建Prometheus数据存储目录
3.安装Prometheus server服务
3.1创建configmap用来存放Prometheus配置信息
1）创建yaml文件
2）应用并查看
3.2 通过deployment部署prometheus
1）上传所需镜像
2）编写yaml文件
3）应用并查看
3.3给Prometheus pod创建一个service
1）编写yaml文件
2）应用并查看
3）结果测试
3.4 Prometheus 热加载
三、Grafana的安装和配置
1.Grafana介绍
2.安装和Grafana
1）上传镜像
2）编写yaml文件
3）应用并查看
3.Grafana接入Prometheus数据源
3.1 浏览器访问
经上述查看，映射端口为30244，在浏览器输入IP:端口号即可访问
3.2 配置grafana界面
1）选择Create your first data source
2）导入监控模板
3）导入docker_rev1.json监控模板
4）如果 Grafana 导入 Prometheusz 之后，发现仪表盘没有数据，如何排查？
四、安装kube-state-metrics组件
1.介绍kube-state-metrics组件
2.安装kube-state-metrics组件
1）创建sa并对其授权
2）上传镜像
3）编写yaml文件并应用
4）创建service
5）导入监控模板
五、配置alertmanager组件
1.创建alertmanager-cm.yaml配置文件
2.Prometheus报警流程
3.创建Prometheus和告警规则的配置文件
1）下图是配置文件中需要修改的地方：
2）删除上次设置的configmap配置
3）配置文件configmap
4）应用配置文件
4.安装Prometheus和altermanager
4.1 安装
1）删除上述操作步骤安装的Prometheus的deployment资源
2）生成etcd-certs
3）拉取镜像
4）编写deployment的yaml文件并应用
5）创建alertmanager的service以便于访问
6）浏览器访问测试
4.2 访问web界面查看效果
1）访问Prometheus的web页面
2）修改配置文件
3）再次访问web页面
一、安装node-exporter
1.下载所需镜像
# 我直接用的镜像压缩包，上传到服务器然后docker load
# 所有节点都要有这个镜像
[root@k8s-master ~]# docker load -i node-exporter.tar.gz
ad68498f8d86: Loading layer [==================================================>] 4.628MB/4.628MB
ad8512dce2a7: Loading layer [==================================================>] 2.781MB/2.781MB
cc1adb06ef21: Loading layer [==================================================>] 16.9MB/16.9MB
Loaded image: prom/node-exporter:v0.16.02.编写node-export.yaml文件并应用
[root@k8s-master node-exporter]# vim node-export.yaml
[root@k8s-master node-exporter]# cat node-export.yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:name: node-exporternamespace: monitor-sa #记得创建命名空间，否则后面会出错labels:name: node-exporter
spec:selector:matchLabels:name: node-exportertemplate:metadata:labels:name: node-exporterspec:hostPID: true #表示pod中的容器可以直接使用主机的网络，与宿主机进行通信hostIPC: truehostNetwork: true #会直接将宿主机的9100端口映射出来，不需要创建servicecontainers:- name: node-exporterimage: prom/node-exporter:v0.16.0ports:- containerPort: 9100resources:requests:cpu: 0.15 #容器运行至少需要0.15核CPUsecurityContext:privileged: true #开启特权模式args:- --path.procfs #配置挂载宿主机的路径- /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: /
[root@k8s-master node-exporter]# kubectl apply -f node-export.yaml
Error from server (NotFound): error when creating "node-export.yaml": namespaces "monitor-sa" not found #命名空间没有创建；
[root@k8s-master node-exporter]# kubectl create ns monitor-sa
namespace/monitor-sa created
[root@k8s-master node-exporter]# kubectl apply -f node-export.yaml
daemonset.apps/node-exporter created# 查看创建好的pod；发现IP与宿主机IP相同
[root@k8s-master node-exporter]# kubectl get pod -n monitor-sa -o wide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
node-exporter-fdvjc 1/1 Running 0 8m21s 192.168.22.136 k8s-node2 <none> <none>
node-exporter-gzfnq 1/1 Running 0 8m21s 192.168.22.134 k8s-master <none> <none>
node-exporter-r85gw 1/1 Running 0 8m21s 192.168.22.135 k8s-node1 <none> <none>
3.测试node-exporter并获取数据
# 通过curl 宿主机IP：9100/metrics 采集数据
# 我访问的是node1节点的CPU[root@k8s-master node-exporter]# curl 192.168.22.135:9100/metrics | grep node_cpu_seconds% Total % Received % Xferd Average Speed Time Time Time CurrentDload Upload Total Spent Left Speed
100 74373 100 74373 0 0 413k 0 --:--:-- --:--:-- --:--:-- 417k
# HELP node_cpu_seconds_total Seconds the cpus spent in each mode. #解释当前指标的含义
# TYPE node_cpu_seconds_total counter #说明当前指标的数据类型
node_cpu_seconds_total{cpu="0",mode="idle"} 18145.96
node_cpu_seconds_total{cpu="0",mode="iowait"} 1.43
node_cpu_seconds_total{cpu="0",mode="irq"} 0
node_cpu_seconds_total{cpu="0",mode="nice"} 0.05
node_cpu_seconds_total{cpu="0",mode="softirq"} 29.26
node_cpu_seconds_total{cpu="0",mode="steal"} 0
node_cpu_seconds_total{cpu="0",mode="system"} 443.06
node_cpu_seconds_total{cpu="0",mode="user"} 383.4
node_cpu_seconds_total{cpu="1",mode="idle"} 18073.89
node_cpu_seconds_total{cpu="1",mode="iowait"} 1.23
node_cpu_seconds_total{cpu="1",mode="irq"} 0
node_cpu_seconds_total{cpu="1",mode="nice"} 0.02
node_cpu_seconds_total{cpu="1",mode="softirq"} 61.35
node_cpu_seconds_total{cpu="1",mode="steal"} 0
node_cpu_seconds_total{cpu="1",mode="system"} 446.99
node_cpu_seconds_total{cpu="1",mode="user"} 361.69# node1节点的负载情况[root@k8s-master node-exporter]# curl 192.168.22.135:9100/metrics | grep node_load% Total % Received % Xferd Average Speed Time Time Time CurrentDload Upload Total Spent Left Speed0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0# HELP node_load1 1m load average.
# TYPE node_load1 gauge
node_load1 0.1 #最近一分钟以内的负载情况
# HELP node_load15 15m load average.
# TYPE node_load15 gauge
node_load15 0.09
# HELP node_load5 5m load average.
# TYPE node_load5 gauge
node_load5 0.04
100 74460 100 74460 0 0 6343k 0 --:--:-- --:--:-- --:--:-- 6610k二、Prometheus server安装和配置
1.创建sa(serviceaccount)账号，对sa做rabc授权
1）创建一个 sa 账号 monitor
[root@k8s-master node-exporter]# kubectl create serviceaccount monitor -n monitor-sa
serviceaccount/monitor created2）把 sa 账号 monitor 通过 clusterrolebing 绑定到 clusterrole 上
[root@k8s-master node-exporter]# kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
clusterrolebinding.rbac.authorization.k8s.io/monitor-clusterrolebinding created2.创建Prometheus数据存储目录
# 在node1节点创建目录[root@k8s-node1 ~]# mkdir /data
[root@k8s-node1 ~]# chmod 777 /data3.安装Prometheus server服务
3.1创建configmap用来存放Prometheus配置信息
1）创建yaml文件
[root@k8s-master yaml]# vim prometheus-cfg.yaml
[root@k8s-master yaml]# cat prometheus-cfg.yaml
---
kind: ConfigMap
apiVersion: v1
metadata:labels:app: prometheusname: prometheus-confignamespace: monitor-sa
data:prometheus.yml: |global:scrape_interval: 15s #采集目标主机监控数据的时间间隔scrape_timeout: 10s #数据采集超时时间，默认10秒evaluation_interval: 1m #触发告警检测的时间，默认是1mscrape_configs: #配置数据源，称为target，每个target用job_name命名- job_name: 'kubernetes-node'kubernetes_sd_configs: #使用的是k8s的服务发现- role: node #使用node角色，它使用默认的kubelet提供的http端口来发现集群中的每个node节点relabel_configs: #重新标记- source_labels: [__address__] #配置的原始标签，匹配地址regex: '(.*):10250' #匹配带有10250端口的urlreplacement: '${1}:9100' #把匹配到的 ip:10250 的 ip 保留target_label: __address__ #新生成的 url 是${1}获取到的 ip:9100action: replace- action: labelmapregex: __meta_kubernetes_node_label_(.+)- job_name: 'kubernetes-node-cadvisor' # 抓取 cAdvisor 数据，是获取 kubelet 上/metrics/cadvisor 接口数据来获取容器的资源使用情况kubernetes_sd_configs:- role: nodescheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crtbearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/tokenrelabel_configs:- action: labelmap #把匹配到的标签保留regex: __meta_kubernetes_node_label_(.+)- target_label: __address__replacement: kubernetes.default.svc:443- source_labels: [__meta_kubernetes_node_name]regex: (.+)target_label: __metrics_path__replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor- job_name: 'kubernetes-apiserver'kubernetes_sd_configs:- role: endpoints #使用 k8s 中的 endpoint 服务发现，采集 apiserver 6443 端口获取到的数据scheme: 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] #endpoint 这个对象的名称空间，endpoint 对象的服务名，exnpoint 的端口名称action: keep #采集满足条件的实例，其他实例不采集regex: 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 # 重新打标仅抓取到的具有 "prometheus.io/scrape: true" 的 annotation 的端点，意思是说如果某个 service 具有 prometheus.io/scrape = true annotation 声明则抓取- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]action: replacetarget_label: __scheme__regex: (https?) #重新设置 scheme，匹配源标签__meta_kubernetes_service_annotation_prometheus_io_scheme 也就是 prometheus.io/scheme annotation，如果源标签的值匹配到 regex，则把值替换为__scheme__对应的值- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]action: replacetarget_label: __metrics_path__regex: (.+) # 应用中自定义暴露的指标,不过这里写的要和 service 中做好约定，如果 service 中这样写 prometheus.io/app-metricspath: '/metrics' 那么你这里就要
__meta_kubernetes_service_annotation_prometheus_io_app_metrics_path 这样写- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]action: replacetarget_label: __address__regex: ([^:]+)(?::\d+)?;(\d+)replacement: $1:$2 #暴露自定义的应用的端口，就是把地址和你在 service 中定义的 "prometheus.io/port = <port>" 声明做一个拼接，然后赋值给__address__，这样 prometheus 就能获取自定义应用的端口，然后通过这个端口再结合__metrics_path__来获取指标- action: labelmap #保留下面匹配到的标签regex: __meta_kubernetes_service_label_(.+)- source_labels: [__meta_kubernetes_namespace]action: replace #替换__meta_kubernetes_namespace 变成 kubernetes_namespacetarget_label: kubernetes_namespace- source_labels: [__meta_kubernetes_service_name]action: replacetarget_label: kubernetes_name 2）应用并查看
[root@k8s-master yaml]# kubectl apply -f prometheus-cfg.yaml
configmap/prometheus-config created
[root@k8s-master yaml]# kubectl get cm -n monitor-sa
NAME DATA AGE
prometheus-config 1 48m3.2 通过deployment部署prometheus
1）上传所需镜像
# node1节点，定义的yaml文件中指定了k8s-node1节点[root@k8s-node1 ~]# docker load -i prometheus-2-2-1.tar.gz
6a749002dd6a: Loading layer 1.338MB/1.338MB
5f70bf18a086: Loading layer 1.024kB/1.024kB
1692ded805c8: Loading layer 2.629MB/2.629MB
035489d93827: Loading layer 66.18MB/66.18MB
8b6ef3a2ab2c: Loading layer 44.5MB/44.5MB
ff98586f6325: Loading layer 3.584kB/3.584kB
017a13aba9f4: Loading layer 12.8kB/12.8kB
4d04d79bb1a5: Loading layer 27.65kB/27.65kB
75f6c078fa6b: Loading layer 10.75kB/10.75kB
5e8313e8e2ba: Loading layer 6.144kB/6.144kB
Loaded image: prom/prometheus:v2.2.12）编写yaml文件
[root@k8s-master yaml]# vim prometheus-deploy.yaml
[root@k8s-master yaml]# cat prometheus-deploy.yaml
---
apiVersion: apps/v1
kind: Deployment
metadata:name: prometheus-servernamespace: monitor-salabels:app: prometheus
spec:replicas: 1selector: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:nodeName: k8s-node1serviceAccountName: monitorcontainers:- name: prometheusimage: prom/prometheus:v2.2.1imagePullPolicy: IfNotPresentcommand:- prometheus- --config.file=/etc/prometheus/prometheus.yml- --storage.tsdb.path=/prometheus #旧数据存储目录- --storage.tsdb.retention=720h #何时删除旧数据，默认为 15 天- --web.enable-lifecycle #开启热加载ports:- 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: Directory3）应用并查看
[root@k8s-master yaml]# kubectl apply -f prometheus-deploy.yaml
deployment.apps/prometheus-server created
[root@k8s-master yaml]# kubectl get deploy -n monitor-sa
NAME READY UP-TO-DATE AVAILABLE AGE
prometheus-server 1/1 1 1 26s3.3给Prometheus pod创建一个service
1）编写yaml文件
[root@k8s-master yaml]# vim prometheus-svc.yaml
[root@k8s-master yaml]# cat prometheus-svc.yaml
apiVersion: v1
kind: Service
metadata:name: prometheusnamespace: monitor-salabels:app: prometheus
spec:type: NodePortports:- port: 9090targetPort: 9090protocol: TCPselector:app: prometheuscomponent: server2）应用并查看
[root@k8s-master yaml]# kubectl apply -f prometheus-svc.yaml
service/prometheus created
[root@k8s-master yaml]# kubectl get svc -n monitor-sa
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
prometheus NodePort 10.104.137.10 <none> 9090:30481/TCP 12s3）结果测试
通过查询可以看到service在宿主机上映射的端口是30481，访问k8s集群的node1节点的IP:端口/graph，就可以访问到web ui界面
点击上方的Status中的Targets，可以看到以下界面：
3.4 Prometheus 热加载
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，想要使配置生效可用如下热加载命令
curl -X POST http://podIP:9090/-/reload
三、Grafana的安装和配置
1.Grafana介绍
Grafana 是一个跨平台的开源的度量分析和可视化工具，可以将采集的数据可视化的展示，并及时通知给告警接收方。它主要有以下特点：
1）展示方式：快速灵活的客户端图表，面板插件有许多不同方式的可视化指标和日志，官方库中具 有丰富的仪表盘插件，比如热图、折线图、图表等多种展示方式；
2）数据源：Graphite，InfluxDB，OpenTSDB，Prometheus，Elasticsearch，CloudWatch 和 KairosDB 等；
3）通知提醒：以可视方式定义最重要指标的警报规则，Grafana 将不断计算并发送通知，在数据达 到阈值时通过 Slack、PagerDuty 等获得通知；
4）混合展示：在同一图表中混合使用不同的数据源，可以基于每个查询指定数据源，甚至自定义数 据源；
5）注释：使用来自不同数据源的丰富事件注释图表，将鼠标悬停在事件上会显示完整的事件元数据 和标记
2.安装和Grafana
1）上传镜像
# node1节点[root@k8s-node1 images-prometheus]# docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
6816d98be637: Loading layer 4.642MB/4.642MB
523feee8e0d3: Loading layer 161.5MB/161.5MB
43d2638621da: Loading layer 230.4kB/230.4kB
f24c0fa82e54: Loading layer 2.56kB/2.56kB
334547094992: Loading layer 5.826MB/5.826MB
Loaded image: k8s.gcr.io/heapster-grafana-amd64:v5.0.42）编写yaml文件
[root@k8s-master yaml]# vim grafana.yaml
[root@k8s-master yaml]# cat grafana.yaml
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:nodeName: k8s-node1containers:- 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
3）应用并查看
[root@k8s-master yaml]# kubectl apply -f grafana.yaml
deployment.apps/monitoring-grafana created
service/monitoring-grafana created
[root@k8s-master yaml]# kubectl get pod -n kube-system -o wide | grep monitor
monitoring-grafana-7979b958c7-rxcw7 1/1 Running 0 64s 10.244.1.23 k8s-node1 <none> <none>
[root@k8s-master yaml]# kubectl get svc -n kube-system
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kube-dns ClusterIP 10.96.0.10 <none> 53/UDP,53/TCP,9153/TCP 3d16h
monitoring-grafana NodePort 10.107.203.6 <none> 80:30244/TCP 3m48s
3.Grafana接入Prometheus数据源
3.1 浏览器访问
经上述查看，映射端口为30244，在浏览器输入IP:端口号即可访问
3.2 配置grafana界面
1）选择Create your first data source
2）导入监控模板
监控模板链接：https://grafana.com/dashboards
此处导入的是node_exporter.json文件
3）导入docker_rev1.json监控模板
跟上一步操作一样
4）如果 Grafana 导入 Prometheusz 之后，发现仪表盘没有数据，如何排查？
打开 grafana 界面，找到仪表盘对应无数据的图标
node_memory_MemTotal_bytes就是grafana上采集的内存数据，需要到Prometheus ui界面看看指标是否是相同的
四、安装kube-state-metrics组件
1.介绍kube-state-metrics组件
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
2.安装kube-state-metrics组件
1）创建sa并对其授权
[root@k8s-master yaml]# vim kube-state-metrics-rbac.yaml
[root@k8s-master yaml]# cat kube-state-metrics-rbac.yaml
---
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
[root@k8s-master yaml]# kubectl apply -f kube-state-metrics-rbac.yaml
serviceaccount/kube-state-metrics created
clusterrole.rbac.authorization.k8s.io/kube-state-metrics created
clusterrolebinding.rbac.authorization.k8s.io/kube-state-metrics created
[root@k8s-master yaml]# kubectl get sa -n kube-system | grep state
kube-state-metrics 1 11m2）上传镜像
[root@k8s-node1 images-prometheus]# docker load -i kube-state-metrics_1_9_0.tar.gz
932da5156413: Loading layer 3.062MB/3.062MB
bd8df7c22fdb: Loading layer 31MB/31MB
Loaded image: quay.io/coreos/kube-state-metrics:v1.9.0
[root@k8s-node1 images-prometheus]# docker images | grep state
quay.io/coreos/kube-state-metrics v1.9.0 101b910a2162 4 years ago 32.8MB
3）编写yaml文件并应用
[root@k8s-master yaml]# vim kube-state-metrics-deploy.yaml
[root@k8s-master yaml]# cat kube-state-metrics-deploy.yaml
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:nodeName: k8s-node1serviceAccountName: kube-state-metricscontainers:- name: kube-state-metricsimage: quay.io/coreos/kube-state-metrics:v1.9.0ports:- containerPort: 8080
[root@k8s-master yaml]# kubectl apply -f kube-state-metrics-deploy.yaml
deployment.apps/kube-state-metrics created
[root@k8s-master yaml]# kubectl get pod -n kube-system | grep kube-state
kube-state-metrics-7684896db9-l5vsz 1/1 Running 0 61s
4）创建service
[root@k8s-master yaml]# vim kube-state-metrics-svc.yaml
[root@k8s-master yaml]# cat kube-state-metrics-svc.yaml
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
[root@k8s-master yaml]# kubectl apply -f kube-state-metrics-svc.yaml
service/kube-state-metrics created
[root@k8s-master yaml]# kubectl get svc -n kube-system
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kube-dns ClusterIP 10.96.0.10 <none> 53/UDP,53/TCP,9153/TCP 3d18h
kube-state-metrics ClusterIP 10.104.238.225 <none> 8080/TCP 19s
monitoring-grafana NodePort 10.107.203.6 <none> 80:30244/TCP 148m
[root@k8s-master yaml]#
5）导入监控模板
两个模板：
Kubernetes Cluster (Prometheus)-1577674936972.json
Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json
五、配置alertmanager组件
1.创建alertmanager-cm.yaml配置文件
[root@k8s-master yaml]# vim alertmanager-cm.yaml
[root@k8s-master yaml]# cat alertmanager-cm.yaml
kind: ConfigMap
apiVersion: v1
metadata:name: alertmanagernamespace: monitor-sa
data:alertmanager.yml: |-global:resolve_timeout: 1msmtp_smarthost: 'smtp.163.com:25' #网易163邮箱smtp_from: '198********@163.com' #从哪个邮箱发送邮件smtp_auth_username: '198********' #发送邮件的用户smtp_auth_password: 'YLOPKFRHHONSHHXM' #网易邮箱的授权码，要用自己的smtp_require_tls: falseroute:group_by: [alertname] # 采用哪个标签来作为分组依据group_wait: 10s # 组告警等待时间。也就是告警产生后等待 10s，如果有同组告警一起发出group_interval: 10s # 上下两组发送告警的间隔时间repeat_interval: 10m # 重复发送告警的时间，减少相同邮件的发送频率，默认是 1hreceiver: default-receiverreceivers:- name: 'default-receiver'email_configs:- to: '178******@qq.com' #接受邮件的邮箱，不能跟上面的邮箱相同send_resolved: true
[root@k8s-master yaml]# kubectl apply -f alertmanager-cm.yaml
configmap/alertmanager created
2.Prometheus报警流程
1）Prometheus Server 监控目标主机上暴露的 http 接口（这里假设接口 A），通过 Promethes 配置的'scrape_interval'定义的时间间隔，定期采集目标主机上监控数据。
2）当接口 A 不可用的时候，Server 端会持续的尝试从接口中取数据，直到"scrape_timeout"时间后 停止尝试。这时候把接口的状态变为“DOWN”。
3）Prometheus 同时根据配置的"evaluation_interval"的时间间隔，定期（默认 1min）的对 Alert Rule 进行评估；当到达评估周期的时候，发现接口 A 为 DOWN，即 UP=0 为真，激活Alert，进入“PENDING”状态，并记录当前 active 的时间；
4） 当下一个 alert rule 的评估周期到来的时候，发现 UP=0 继续为真，然后判断警报 Active 的时间是否已经超出 rule 里的‘for’ 持续时间，如果未超出，则进入下一个评估周期；如果时间超出，则 alert 的状态变为“FIRING”；同时调用 Alertmanager 接口，发送相关报警数据。
5）AlertManager 收到报警数据后，会将警报信息进行分组，然后根据 alertmanager 配置的“group_wait”时间先进行等待。等 wait 时间过后再发送报警信息。
6）属于同一个 Alert Group 的警报，在等待的过程中可能进入新的 alert，如果之前的报警已经成 功发出，那么间隔“group_interval”的时间间隔后再重新发送报警信息。比如配置的是邮件报警，那么同属一个 group 的报警信息会汇总在一个邮件里进行发送。
7）如果 Alert Group 里的警报一直没发生变化并且已经成功发送，等待‘repeat_interval’时间间隔之后再重复发送相同的报警邮件；如果之前的警报没有成功发送，则相当于触发第 6 条条件，则需要等待 group_interval 时间间隔后重复发送
3.创建Prometheus和告警规则的配置文件
1）下图是配置文件中需要修改的地方：
2）删除上次设置的configmap配置
# 先删除上次设置的configmap
[root@k8s-master yaml]# kubectl delete -f prometheus-cfg.yaml
configmap "prometheus-config" deleted3）配置文件configmap
# 编写新的configmap配置文件[root@k8s-master yaml]# vim prometheus-alertmanager-cfg.yaml
[root@k8s-master yaml]# cat prometheus-alertmanager-cfg.yaml
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:$2source_labels:- __address__- __meta_kubernetes_pod_annotation_prometheus_io_porttarget_label: __address__- action: labelmapregex: __meta_kubernetes_pod_label_(.+)- action: replacesource_labels:- __meta_kubernetes_namespacetarget_label: kubernetes_namespace- action: replacesource_labels:- __meta_kubernetes_pod_nametarget_label: kubernetes_pod_name- job_name: 'kubernetes-schedule'scrape_interval: 5sstatic_configs:- targets: ['192.168.22.134:10251']- job_name: 'kubernetes-controller-manager'scrape_interval: 5sstatic_configs:- targets: ['192.168.22.134:10252']- job_name: 'kubernetes-kube-proxy'scrape_interval: 5sstatic_configs:- targets: ['192.168.22.134:10249','192.168.22.135:10249','192.168.22.136:10249']- job_name: 'kubernetes-etcd'scheme: httpstls_config:ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crtcert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crtkey_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.keyscrape_interval: 5sstatic_configs:- targets: ['192.168.22.134:2379']rules.yml: |groups:- name: examplerules:- alert: kube-proxy的cpu使用率大于80%expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80for: 2slabels:severity: warnningannotations:description: "{{$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}}%)"4）应用配置文件
[root@k8s-master yaml]# kubectl apply -f prometheus-alertmanager-cfg.yaml
configmap/prometheus-config created
[root@k8s-master yaml]# kubectl get cm -n monitor-sa
NAME DATA AGE
alertmanager 1 25m
prometheus-config 2 3m20s
4.安装Prometheus和altermanager
4.1 安装
1）删除上述操作步骤安装的Prometheus的deployment资源
[root@k8s-master yaml]# kubectl delete -f prometheus-deploy.yaml
deployment.apps "prometheus-server" deleted2）生成etcd-certs
[root@k8s-master yaml]# 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
secret/etcd-certs created[root@k8s-master yaml]# kubectl get secret -n monitor-sa
NAME TYPE DATA AGE
default-token-jjw8z kubernetes.io/service-account-token 3 24h
etcd-certs Opaque 3 40s
monitor-token-jr24f kubernetes.io/service-account-token 3 23h
3）拉取镜像
# 此处我用的node2节点[root@k8s-node2 images-prometheus]# docker load -i alertmanager.tar.gz
4febd3792a1f: Loading layer 1.36MB/1.36MB
68d1a8b41cc0: Loading layer 2.586MB/2.586MB
5f70bf18a086: Loading layer 1.024kB/1.024kB
30d4e7b232e4: Loading layer 12.77MB/12.77MB
6b961451fcb0: Loading layer 16.59MB/16.59MB
b5abc4736d3f: Loading layer 6.144kB/6.144kB
Loaded image: prom/alertmanager:v0.14.0
[root@k8s-node2 images-prometheus]# scp alertmanager.tar.gz k8s-node2:/root/
root@k8s-node2's password:
alertmanager.tar.gz 100% 32MB 16.1MB/s 00:01
[root@k8s-node2 images-prometheus]# docker images | grep alert
prom/alertmanager v0.14.0 23744b2d645c 6 years ago 31.9MB
4）编写deployment的yaml文件并应用
[root@k8s-master yaml]# vim prometheus-alertmanager-deploy.yaml
[root@k8s-master yaml]# cat prometheus-alertmanager-deploy.yaml
---
apiVersion: apps/v1
kind: Deployment
metadata:name: prometheus-servernamespace: monitor-salabels:app: prometheus
spec:replicas: 1selector: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:nodeName: k8s-node1 #此处指定的node1节点serviceAccountName: monitorcontainers:- name: prometheusimage: prom/prometheus:v2.2.1imagePullPolicy: 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:v0.14.0imagePullPolicy: 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# 应用yaml文件[root@k8s-master yaml]# kubectl apply -f prometheus-alertmanager-deploy.yaml
deployment.apps/prometheus-server created
[root@k8s-master yaml]# kubectl get pod -n monitor-sa
NAME READY STATUS RESTARTS AGE
node-exporter-fdvjc 1/1 Running 1 24h
node-exporter-gzfnq 1/1 Running 0 24h
node-exporter-r85gw 1/1 Running 0 24h
prometheus-server-6c5bc4d65b-9qzn6 2/2 Running 0 39s5）创建alertmanager的service以便于访问
[root@k8s-master yaml]# vim alertmanager-svc.yaml
[root@k8s-master yaml]# cat alertmanager-svc.yaml
---
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
[root@k8s-master yaml]# kubectl apply -f alertmanager-svc.yaml
service/alertmanager created
[root@k8s-master yaml]# kubectl get svc -n monitor-sa
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
alertmanager NodePort 10.98.208.193 <none> 9093:30066/TCP 16s
prometheus NodePort 10.104.137.10 <none> 9090:30481/TCP 20h
6）浏览器访问测试
通过上述查询，可以看到Prometheus映射的端口为30481，alertmanager映射的端口为30066；浏览器输入192.168.22.135:30066/#/alerts访问；
也就是http://node1节点IP:端口号/#/alerts
4.2 访问web界面查看效果
1）访问Prometheus的web页面
点击status中的targets；
2）修改配置文件
# kube-schedule：vim /etc/kubernetes/manifests/kube-scheduler.yaml#将里面的--bind-address=127.0.0.1改成192.168.22.134；--port=0删除；
#把httpGet：下面的hosts改成192.168.22.134
# 注意是改成master节点的IP# kube-controller-managervim /etc/kubernetes/manifests/kube-controller-manager.yaml#将里面的--bind-address=127.0.0.1改成192.168.22.134；--port=0删除；
#把httpGet：下面的hosts改成192.168.22.134
# 注意是改成master节点的IP# 改完之后重启kubelet# 查看服务：kubectl get cs ；status都是healthy即可# kube-proxykubectl edit configmap kube-proxy -n kube-system# 把metricsBindAddress这段修改成metricsBindAddress: 0.0.0.0:10249#然后再删除pod重新创建即可
kubectl get pods -n kube-system | grep kube-proxy |awk '{print $1}' | xargs kubectl delete pods -n kube-system
3）再次访问web页面
