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-exporter
namespace: 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-exporter
template:
metadata:
labels:
name: node-exporter
spec:
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: true
hostNetwork: true #会直接将宿主机的9100端口映射出来，不需要创建service
containers:
- name: node-exporter
image: prom/node-exporter:v0.16.0
ports:
- containerPort: 9100
resources:
requests:
cpu: 0.15 #容器运行至少需要0.15核CPU
securityContext:
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: dev
mountPath: /host/dev
- name: proc
mountPath: /host/proc
- name: sys
mountPath: /host/sys
- name: rootfs
mountPath: /rootfs
tolerations:
- key: "node-role.kubernetes.io/master"
operator: "Exists"
effect: "NoSchedule"
volumes:
- name: proc
hostPath:
path: /proc
- name: dev
hostPath:
path: /dev
- name: sys
hostPath:
path: /sys
- name: rootfs
hostPath:
path: /
[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 Current
Dload 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 Current
Dload Upload Total Spent Left Speed
0 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: prometheus
name: prometheus-config
namespace: 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1m
scrape_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url
replacement: '${1}:9100' #把匹配到的 ip:10250 的 ip 保留
target_label: __address__ #新生成的 url 是${1}获取到的 ip:9100
action: replace
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- job_name: 'kubernetes-node-cadvisor' # 抓取 cAdvisor 数据，是获取 kubelet 上/metrics/cadvisor 接口数据来获取容器的资源使用情况
kubernetes_sd_configs:
- role: node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap #把匹配到的标签保留
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: /api/v1/nodes/${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: 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] #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: endpoints
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: 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: replace
target_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: replace
target_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: replace
target_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_namespace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_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:v2.2.1
2）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-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: k8s-node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
imagePullPolicy: IfNotPresent
command:
- prometheus
- --config.file=/etc/prometheus/prometheus.yml
- --storage.tsdb.path=/prometheus #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: 9090
protocol: TCP
volumeMounts:
- mountPath: /etc/prometheus/prometheus.yml
name: prometheus-config
subPath: prometheus.yml
- mountPath: /prometheus/
name: prometheus-storage-volume
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
items:
- key: prometheus.yml
path: prometheus.yml
mode: 0644
- name: prometheus-storage-volume
hostPath:
path: /data
type: 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: prometheus
namespace: monitor-sa
labels:
app: prometheus
spec:
type: NodePort
ports:
- port: 9090
targetPort: 9090
protocol: TCP
selector:
app: prometheus
component: 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-grafana
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
task: monitoring
k8s-app: grafana
template:
metadata:
labels:
task: monitoring
k8s-app: grafana
spec:
nodeName: k8s-node1
containers:
- name: grafana
image: k8s.gcr.io/heapster-grafana-amd64:v5.0.4
ports:
- containerPort: 3000
protocol: TCP
volumeMounts:
- mountPath: /etc/ssl/certs
name: ca-certificates
readOnly: true
- mountPath: /var
name: grafana-storage
env:
- name: INFLUXDB_HOST
value: monitoring-influxdb
- name: GF_SERVER_HTTP_PORT
value: "3000"
# The following env variables are required to make Grafana accessible via
# the kubernetes api-server proxy. On production clusters, we recommend
# removing these env variables, setup auth for grafana, and expose the grafana
# service using a LoadBalancer or a public IP.
- name: GF_AUTH_BASIC_ENABLED
value: "false"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ORG_ROLE
value: Admin
- name: GF_SERVER_ROOT_URL
# If you're only using the API Server proxy, set this value instead:
# value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
value: /
volumes:
- name: ca-certificates
hostPath:
path: /etc/ssl/certs
- name: grafana-storage
emptyDir: {}
---
apiVersion: 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-grafana
name: monitoring-grafana
namespace: 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: NodePort
ports:
- port: 80
targetPort: 3000
selector:
k8s-app: grafana
type: 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-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
[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 11m
2）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-metrics
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
app: kube-state-metrics
template:
metadata:
labels:
app: kube-state-metrics
spec:
nodeName: k8s-node1
serviceAccountName: kube-state-metrics
containers:
- name: kube-state-metrics
image: quay.io/coreos/kube-state-metrics:v1.9.0
ports:
- 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-metrics
namespace: kube-system
labels:
app: kube-state-metrics
spec:
ports:
- name: kube-state-metrics
port: 8080
protocol: TCP
selector:
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: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_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: false
route:
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 1h
receiver: default-receiver
receivers:
- 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: 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:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['192.168.22.134:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['192.168.22.134:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['192.168.22.134:10249','192.168.22.135:10249','192.168.22.136:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['192.168.22.134:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: kube-proxy的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: scheduler的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: scheduler的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: controller-manager的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: controller-manager的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: apiserver的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: apiserver的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: etcd的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: etcd的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: kube-state-metrics的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: kube-state-metrics的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: coredns的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: coredns的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: kube-proxy打开句柄数>600
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kube-proxy打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>600
expr: process_open_fds{job=~"kubernetes-schedule"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-schedule"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>600
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>600
expr: process_open_fds{job=~"kubernetes-apiserver"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-apiserver"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>600
expr: process_open_fds{job=~"kubernetes-etcd"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-etcd"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"
value: "{{ $value }}"
- alert: kube-proxy
expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: scheduler
expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-controller-manager
expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-apiserver
expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-etcd
expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kube-dns
expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: HttpRequestsAvg
expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m])) > 1000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"
value: "{{ $value }}"
threshold: "1000"
- alert: Pod_restarts
expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
for: 2s
labels:
severity: warnning
annotations:
description: "在{{$labels.namespace}}名称空间下发现{{$labels.pod}}这个pod下的容器{{$labels.container}}被重启,这个监控指标是由{{$labels.instance}}采集的"
value: "{{ $value }}"
threshold: "0"
- alert: Pod_waiting
expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"
value: "{{ $value }}"
threshold: "1"
- alert: Pod_terminated
expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"
value: "{{ $value }}"
threshold: "1"
- alert: Etcd_leader
expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_leader_changes
expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_failed
expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_db_total_size
expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}})：db空间超过10G"
value: "{{ $value }}"
threshold: "10G"
- alert: Endpoint_ready
expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.endpoint}}不可用"
value: "{{ $value }}"
threshold: "1"
- name: 物理节点状态-监控告警
rules:
- alert: 物理节点cpu使用率
expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90
for: 2s
labels:
severity: ccritical
annotations:
summary: "{{ $labels.instance }}cpu使用率过高"
description: "{{ $labels.instance }}的cpu使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: 物理节点内存使用率
expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}内存使用率过高"
description: "{{ $labels.instance }}的内存使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: InstanceDown
expr: up == 0
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}: 服务器宕机"
description: "{{ $labels.instance }}: 服务器延时超过2分钟"
- alert: 物理节点磁盘的IO性能
expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入磁盘IO使用率过高！"
description: "{{$labels.mountpoint }} 流入磁盘IO大于60%(目前使用:{{$value}})"
- alert: 入网流量带宽
expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入网络带宽过高！"
description: "{{$labels.mountpoint }}流入网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: 出网流量带宽
expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流出网络带宽过高！"
description: "{{$labels.mountpoint }}流出网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: TCP会话
expr: node_netstat_Tcp_CurrEstab > 1000
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高！"
description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"
- alert: 磁盘容量
expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 磁盘分区使用率过高！"
description: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$value}}%)"
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-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: k8s-node1 #此处指定的node1节点
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
# 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 39s
5）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: 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
[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-manager
vim /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-proxy
kubectl 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
