Prometheus+Grafana+Alertmanager构建企业级监控系统

Prometheus+Grafana+Alertmanager构建企业级监控系统

环境

1. 安装node-exporter

1.1 node-exporter介绍

node-exporter 可以采集机器（物理机、虚拟机、云主机等）的监控指标数据，能够采集到的指标包

括 CPU, 内存，磁盘，网络，文件数等信息。

1.2 安装node-exporter

#查看一下k8s节点
[root@master1 .kube]# kubectl get nodes
NAME
STATUS
ROLES
AGE
VERSION
master1
Ready
control-plane,master
12d
v1.20.6
node1
Ready
worker
12d
v1.20.6
#创建一个monitor-sa命名空间
[root@master1 .kube]# kubectl create namespace monitor-sa
namespace/monitor-sa created
#上传node-exporter.tar.gz到master1和node1的家目录
[root@master1 ~]# 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:v0.16.0
[root@master1 ~]# 
[root@node1 ~]# 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:v0.16.0
[root@node1 ~]# 
#说明 获取node-exporter的方法
在dockerhub的官网搜索
https://hub.docker.com/

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[root@master1 prometheus]# cat > /root/prometheus/node-export.yaml <<END
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: node-exporter
namespace: monitor-sa
labels:
name: node-exporter
spec:
selector:
matchLabels:
name: node-exporter
template:
metadata:
labels:
name: node-exporter
spec:
hostPID: true
hostIPC: true
hostNetwork: true
containers:
- name: node-exporter
image: prom/node-exporter:v0.16.0
ports:
- containerPort: 9100
resources:
requests:
cpu: 0.15
securityContext:
privileged: true
args:
- --path.procfs
- /host/proc
- --path.sysfs
- /host/sys
- --collector.filesystem.ignored-mount-points
- '"^/(sys|proc|dev|host|etc)($|/)"'
volumeMounts:
- name: dev
mountPath: /host/dev
- name: proc
mountPath: /host/proc
- name: sys
mountPath: /host/sys
- name: rootfs
mountPath: /rootfs
tolerations:
- key: "node-role.kubernetes.io/master"
operator: "Exists"
effect: "NoSchedule"
volumes:
- name: proc
hostPath:
path: /proc
- name: dev
hostPath:
path: /dev
- name: sys
hostPath:
path: /sys
- name: rootfs
hostPath:
path: /
END
[root@master1 prometheus]# kubectl apply -f node-export.yaml 
daemonset.apps/node-exporter created
[root@master1 prometheus]# kubectl get pods -n monitor-sa -o wide
NAME
READY
STATUS
RESTARTS
AGE
IP
NODE
NOMINATED NODE
READINESS GATES
node-exporter-92k4d
1/1
Running
0
58s
192.168.1.181
node1
<none>
<none>
node-exporter-d44k4
1/1
Running
0
58s
192.168.1.180
master1
<none>
<none>

1.3 通过 node-exporter 采集数据

curl http://主机 ip:9100/metrics
#node-export 默认的监听端口是 9100，可以看到当前主机获取到的所有监控数据

curl http://192.168.1.180:9100/metrics | grep node_cpu_seconds
显示 192.168.1.180 主机 cpu 的使用情况
**# HELP node_cpu_seconds_total Seconds the cpus spent in each mode.

**# TYPE node_cpu_seconds_total counter

node_cpu_seconds_total{cpu="0",mode="idle"} 72963.37
node_cpu_seconds_total{cpu="0",mode="iowait"} 9.35
node_cpu_seconds_total{cpu="0",mode="irq"} 0
node_cpu_seconds_total{cpu="0",mode="nice"} 0
node_cpu_seconds_total{cpu="0",mode="softirq"} 151.4
node_cpu_seconds_total{cpu="0",mode="steal"} 0
node_cpu_seconds_total{cpu="0",mode="system"} 656.12
node_cpu_seconds_total{cpu="0",mode="user"} 267.1
#HELP：解释当前指标的含义，上面表示在每种模式下 node 节点的 cpu 花费的时间，以 s 为单位

#TYPE：说明当前指标的数据类型，上面是 counter 类型

node_cpu_seconds_total{cpu="0",mode="idle"} ：
cpu0 上 idle 进程占用 CPU 的总时间，CPU 占用时间是一个只增不减的度量指标，从类型中也可以看
出 node_cpu 的数据类型是 counter（计数器）
**counter 计数器：只是采集递增的指标
curl http://192.168.40.180:9100/metrics | grep node_load
**# HELP node_load1 1m load average.

**# TYPE node_load1 gauge 
node_load1 0.1
node_load1 该指标反映了当前主机在最近一分钟以内的负载情况，系统的负载情况会随系统资源的
使用而变化，因此 node_load1 反映的是当前状态，数据可能增加也可能减少，从注释中可以看出当前指
标类型为 gauge（标准尺寸）
gauge 标准尺寸：统计的指标可增加可减少

2. Prometheus server 安装和配置

2.1 创建 sa 账号，对 sa 做 rbac 授权

#创建一个 sa 账号 monitor
[root@master1 prometheus]# kubectl create serviceaccount monitor -n monitor-sa
serviceaccount/monitor created
[root@master1 prometheus]# kubectl get serviceaccount -n monitor-sa
NAME
SECRETS
AGE
default
1
79m
monitor
1
30s
#把 serviceaccount 账号 monitor 通过 clusterrolebinding 绑定到 clusterrole 上
[root@master1 prometheus]# kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
clusterrolebinding.rbac.authorization.k8s.io/monitor-clusterrolebinding created

2.2 创建 prometheus 数据存储目录

#在 k8s 集群的 node1 节点上创建数据存储目录
[root@node1 ~]# mkdir /data
[root@node1 ~]# chmod 777 /data/
[root@node1 ~]# ls -ld /data
drwxrwxrwx. 2 root root 6 Jun
8 16:00 /data

2.3 安装prometheus服务

2.3.1 创建一个 configmap 存储卷，用来存放 prometheus 配置信息

[root@master1 prometheus]# cat > /root/prometheus/prometheus-cfg.yaml <<END
---
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10s
evaluation_interval: 1m
#触发告警检测的是境，默认是1m
scrape_configs:
#配置数据源，称为target,每个target用job_name命名。又分为静态配置
#和服务发现
- 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}获取的ip:9100
action: replace
- action: labelmap
#匹配到下面正则表达式的标签会被保留，如果不做regex正则的话，默认只是会显示instance标签
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_(.+)
#保留匹配到的具有__meta_kubernetes_node_label的标签
- target_label: __address__
#获取到的地址： __address__="192.168.1.180:10250"
replacement: kubernetes.default.svc:443
#把获取到的地址替换成新的地址kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
#把原始标签中__meta_kubernetes_node_name值匹配到
target_label: __metrics_path__
#获取__metrics_path__对应的值
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]
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
#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 = ture annotation声明则抓取，annotation本身也是键值结构，所以这里的源标签设置为键，而regex设置值true,当值匹配到regex设定的内容时则执行keep动作也就是保留，其余则丢弃。
- 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，也许你暴露的 API 接口不是/metrics 这个路径，那么你可以在这个POD 对应的 service 中做一个"prometheus.io/path = /mymetrics" 声明，上面的意思就是把你声明的这个路径赋值给__metrics_path__，其实就是让 prometheus 来获取自定义应用暴露的 metrices 的具体路径，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:$2
# 暴露自定义的应用的端口，就是把地址和你在 service 中定义的 "prometheus.io/port = <port>" 声明做一个拼接，然后赋值给__address__，这样 prometheus 就能获取自定义应用的端口，然后通过这个端口再结合__metrics_path__来获取指标，如果__metrics_path__值不是默认的/metrics 那么就要使用上面的标签替换来获取真正暴露的具体路径。
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
END
[root@master1 prometheus]# kubectl apply -f prometheus-cfg.yaml 
configmap/prometheus-config created
[root@master1 prometheus]# kubectl get configmap -n monitor-sa
NAME
DATA
AGE
kube-root-ca.crt
1
3h57m
prometheus-config
1
18s
[root@master1 prometheus]# kubectl describe configmap prometheus-config -n monitor-sa
Name:
prometheus-config
Namespace:
monitor-sa
Labels:
app=prometheus
Annotations:
<none>
Data
====
prometheus.yml:
----
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 1m
scrape_configs:
- job_name: 'kubernetes-node'
kubernetes_sd_configs:
- role: node
relabel_configs:
- source_labels: [__address__]
regex: '(.*):10250'
replacement: '${1}:9100'
target_label: __address__
action: replace
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- job_name: 'kubernetes-node-cadvisor'
kubernetes_sd_configs:
- role:
node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
- job_name: 'kubernetes-apiserver'
kubernetes_sd_configs:
- role: endpoints
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
action: keep
regex: default;kubernetes;https
- job_name: 'kubernetes-service-endpoints'
kubernetes_sd_configs:
- role: endpoints
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
action: replace
target_label: __address__
regex: ([^:]+)(?::d+)?;(d+)
replacement: $1:$2
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
Events:
<none>

2.3.2 通过 deployment 部署 prometheus

安装 prometheus 需要的镜像 prometheus-2-2-1.tar.gz ，上传到 k8s 的工作节点 node1 上，手动解压

这个镜像可以从hub.docker.com dockerhub上下载，也可以通过如下指令pull

docker pull prom/prometheus:v2.2.1

[root@node1 ~]# ls prometheus-2-2-1.tar.gz 
prometheus-2-2-1.tar.gz
[root@node1 ~]# du -sh prometheus-2-2-1.tar.gz 
110M
prometheus-2-2-1.tar.gz
[root@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
[root@node1 ~]# 

[root@master1 prometheus]# cat > /root/prometheus/prometheus-deploy.yaml <<END
---
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:
- prometheus
- --config.file=/etc/prometheus/prometheus.yml
- --storage.tsdb.path=/prometheus
- --storage.tsdb.retention=720h
- --web.enable-lifecycle
##启用prometheus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: Directory
END
[root@master1 prometheus]# kubectl apply -f prometheus-deploy.yaml 
deployment.apps/prometheus-server created
[root@master1 prometheus]# kubectl get pods -n monitor-sa -o wide
NAME
READY
STATUS
RESTARTS
AGE
IP
NODE
NOMINATED NODE
READINESS GATES
node-exporter-92k4d
1/1
Running
0
4h14m
192.168.1.181
node1
<none>
<none>
node-exporter-d44k4
1/1
Running
0
4h14m
192.168.1.180
master1
<none>
<none>
prometheus-server-657bd8cb4d-zrmk4
1/1
Running
0
42s
10.244.166.185
node1
<none>
<none>
[root@master1 prometheus]# 

[root@master1 prometheus]# cat > /root/prometheus/prometheus-svc.yaml <<END
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: server
END
[root@master1 prometheus]# kubectl apply -f prometheus-svc.yaml 
service/prometheus created
[root@master1 prometheus]# kubectl get service -n monitor-sa
NAME
TYPE
CLUSTER-IP
EXTERNAL-IP
PORT(S)
AGE
prometheus
NodePort
10.103.238.66
<none>
9090:31935/TCP
37s
[root@master1 prometheus]# kubectl get endpoints -n monitor-sa
NAME
ENDPOINTS
AGE
prometheus
10.244.166.185:9090
3m50s

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2.4 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：
[root@master1 prometheus]# kubectl get pods -n monitor-sa -o wide -l app=prometheus
NAME
READY
STATUS
RESTARTS
AGE
IP
NODE
NOMINATED NODE
READINESS GATES
prometheus-server-657bd8cb4d-zrmk4
1/1
Running
0
64m
10.244.166.185
node1
<none>
<none>
[root@master1 prometheus]# 
#10.244.166.185是 prometheus 的 pod 的 ip 地址
想要使配置生效可用如下命令热加载：
[root@master1 prometheus]# curl -X POST http://10.244.166.185:9090/-/reload
#热加载速度比较慢，可以暴力重启 prometheus，如修改上面的 prometheus-cfg.yaml 文件之后，可
执行如下强制删除：
kubectl delete -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
然后再通过 apply 更新：
kubectl apply -f prometheus-cfg.yaml
kubectl apply -f prometheus-deploy.yaml
注意：
线上最好热加载，暴力删除可能造成监控数据的丢失

3. 可视化 UI 界面 Grafana 的安装和配置

3.1 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。它主要有以下六大特点：

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。

3.2 安装 Grafana

**安装 Grafana 需要的镜像 heapster-grafana-amd64_v5_0_4.tar.gz,把镜像上传到 k8s 的工作节点

node1 上，手动解压：

[root@node1 prometheus]# ls
heapster-grafana-amd64_v5_0_4.tar.gz
[root@node1 prometheus]# du -sh heapster-grafana-amd64_v5_0_4.tar.gz 
165M
heapster-grafana-amd64_v5_0_4.tar.gz
[root@node1 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:v5.0.4

这个镜像可以在hub.docker.com上搜索下载

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#在控制节点master1上生成如下文件
[root@master1 prometheus]# cat > /root/prometheus/grafana.yaml <<END
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:
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
END
[root@master1 prometheus]# kubectl apply -f grafana.yaml 
deployment.apps/monitoring-grafana created
service/monitoring-grafana created
[root@master1 prometheus]# kubectl get deploy -n kube-system
NAME
READY
UP-TO-DATE
AVAILABLE
AGE
calico-kube-controllers
1/1
1
1
13d
coredns
2/2
2
2
13d
monitoring-grafana
1/1
1
1
27s
[root@master1 prometheus]# kubectl get rs -n kube-system
NAME
DESIRED
CURRENT
READY
AGE
calico-kube-controllers-6949477b58
1
1
1
13d
coredns-7f89b7bc75
2
2
2
13d
monitoring-grafana-675798bf47
1
1
1
37s
[root@master1 prometheus]# kubectl get pods -n kube-system
NAME
READY
STATUS
RESTARTS
AGE
calico-kube-controllers-6949477b58-phvxx
1/1
Running
0
20h
calico-node-n5j7r
1/1
Running
0
13d
calico-node-r26rb
1/1
Running
0
13d
coredns-7f89b7bc75-8h7vd
1/1
Running
0
11d
coredns-7f89b7bc75-txs9t
1/1
Running
0
20h
etcd-master1
1/1
Running
0
13d
fluentd-elasticsearch-6qzdk
1/1
Running
0
3d23h
fluentd-elasticsearch-rxsgw
1/1
Running
0
3d23h
kube-apiserver-master1
1/1
Running
0
13d
kube-controller-manager-master1
1/1
Running
0
13d
kube-proxy-6jdfc
1/1
Running
0
13d
kube-proxy-n4gx7
1/1
Running
0
13d
kube-scheduler-master1
1/1
Running
0
13d
monitoring-grafana-675798bf47-x8sm8
1/1
Running
0
45s
[root@master1 prometheus]# 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
13d
monitoring-grafana
NodePort
10.102.90.185
<none>
80:32738/TCP
55s

使用k8s节点IP加32738可以在浏览器访问了

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3.3 Grafana 界面接入 Prometheus 数据源

查看 grafana 前端的 service

[root@master1 prometheus]# kubectl get service -n kube-system|grep grafana
monitoring-grafana
NodePort
10.102.90.185
<none>
80:32738/TCP
15m

1）登陆grafana,在浏览器访问

192.168.1.180:32738

看到如下 内容

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2）配置 grafana 界面：

开始配置 grafana 的 web 界面：

选择 Create your first data source

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导入的监控模板，可在如下链接搜索

https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes

可直接导入 node_exporter.json 监控模板，这个可以把 node 节点指标显示出来

怎么导入监控模板，按如下步骤：

上面 Save & Test 测试没问题之后，就可以返回 Grafana 主页面,点击左侧的+号下面的Import

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出现如下界面

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可直接导入 docker_rev1.json，显示容器资源指标的，

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4. 安装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

[root@master1 prometheus]# cat > /root/prometheus/kube-state-metrics-rbac.yaml <<END
---
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
END
[root@master1 prometheus]# 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@master1 prometheus]# kubectl get serviceaccount -n kube-system|grep kube-state
kube-state-metrics
1
78s
[root@master1 prometheus]# kubectl get ClusterRole -n kube-system|grep kube-state
kube-state-metrics
2022-06-10T07:03:54Z
[root@master1 prometheus]# kubectl get ClusterRoleBinding -n kube-system|grep kube-state
kube-state-metrics
ClusterRole/kube-state-metrics

2. 安装 kube-state-metrics 组件

安装 kube-state-metrics 组件需要的镜像上传到 k8s 工作节点node1上，手动解压：

这个镜像也可在hub.docker.com上找到

[root@node1 prometheus]# ls kube-state-metrics_1_9_0.tar.gz 
kube-state-metrics_1_9_0.tar.gz
[root@node1 prometheus]# du -sh kube-state-metrics_1_9_0.tar.gz 
33M
kube-state-metrics_1_9_0.tar.gz
[root@node1 prometheus]# 
[root@node1 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

在控制节点上生成yaml文件

[root@master1 prometheus]# cat > /root/prometheus/kube-state-metrics-deploy.yaml <<END
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: quay.io/coreos/kube-state-metrics:v1.9.0
ports:
- containerPort: 8080
END
[root@master1 prometheus]# kubectl apply -f kube-state-metrics-deploy.yaml 
deployment.apps/kube-state-metrics created
[root@master1 prometheus]# kubectl get pods -n kube-system
NAME
READY
STATUS
RESTARTS
AGE
calico-kube-controllers-6949477b58-phvxx
1/1
Running
0
2d1h
calico-node-n5j7r
1/1
Running
0
14d
calico-node-r26rb
1/1
Running
0
14d
coredns-7f89b7bc75-8h7vd
1/1
Running
0
13d
coredns-7f89b7bc75-txs9t
1/1
Running
0
2d1h
etcd-master1
1/1
Running
0
14d
fluentd-elasticsearch-6qzdk
1/1
Running
0
5d4h
fluentd-elasticsearch-rxsgw
1/1
Running
0
5d4h
kube-apiserver-master1
1/1
Running
0
14d
kube-controller-manager-master1
1/1
Running
0
14d
kube-proxy-6jdfc
1/1
Running
0
14d
kube-proxy-n4gx7
1/1
Running
0
14d
kube-scheduler-master1
1/1
Running
0
14d
kube-state-metrics-58d4957bc5-v6tn2
1/1
Running
0
7m38s
monitoring-grafana-675798bf47-x8sm8
1/1
Running
0
29h

3. 创建service

[root@master1 prometheus]# cat > /root/prometheus/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
END
[root@master1 prometheus]# kubectl apply -f kube-state-metrics-svc.yaml
service/kube-state-metrics created
[root@master1 prometheus]# 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
15d
kube-state-metrics
ClusterIP
10.102.207.144
<none>
8080/TCP
46s
monitoring-grafana
NodePort
10.102.90.185
<none>
80:32738/TCP
29h
[root@master1 prometheus]# kubectl get endpoints -n kube-system
NAME
ENDPOINTS
AGE
kube-dns
10.244.137.66:53,10.244.137.69:53,10.244.137.66:53 + 3 more...
15d
kube-state-metrics
10.244.166.187:8080
71s
monitoring-grafana
10.244.166.186:3000
29h

在 grafana web 界面导入 Kubernetes Cluster (Prometheus)-1577674936972.json 和 Kubernetes

cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json

文件在grafana.com上找到

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导入json

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5. 配置 alertmanager-发送报警到 qq 邮箱

报警：指 prometheus 将监测到的异常事件发送给 alertmanager

通知：alertmanager 将报警信息发送到邮件、微信、钉钉等

创建 alertmanager 配置文件

在 k8s 的控制节点master1创建 alertmanager-cm.yaml 文件

[root@master1 prometheus]# ls alertmanager-cm.yaml 
alertmanager-cm.yaml
[root@master1 prometheus]# du -sh alertmanager-cm.yaml 
4.0K
alertmanager-cm.yaml
[root@master1 prometheus]# 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'
smtp_from: 'xxxxxxxx@163.com'
smtp_auth_username: 'xxxxxxxx'
smtp_auth_password: 'xxxxxxxxxx'
smtp_require_tls: false
route:
#用于配置告警分发策略
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
##定义谁来接收告警
receivers:
- name: 'default-receiver'
email_configs:
- to: 'xxxxxxxx@qq.com'
send_resolved: true
[root@master1 prometheus]# 
[root@master1 prometheus]# kubectl apply -f alertmanager-cm.yaml 
configmap/alertmanager created
[root@master1 prometheus]# kubectl get cm -n monitor-sa
NAME
DATA
AGE
alertmanager
1
23s
alertmanager 配置文件解释说明：
smtp_smarthost: 'smtp.163.com:25'
#163 邮箱的 SMTP 服务器地址+端口
smtp_from: 'xxxxxxxx@163.com'
#这是指定从哪个邮箱发送报警
smtp_auth_username: 'xxxxxxxx' #这是发送邮箱的认证用户，不是邮箱名
smtp_auth_password: 'xxxxxxxxxx' #这是发送邮箱的授权码而不是登录密码，你们需要用自己的，不要用我的，用我的你会发不出来报警
email_configs:
- to: 'xxxxxxxxx@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
route: #用于设置告警的分发策略
group_by: [alertname]
#alertmanager 会根据 group_by 配置将 Alert 分组
group_wait: 10s
# 分组等待时间。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 #定义谁来收告警

Prometheus 一条告警的触发流程、等待时间

报警处理流程如下：

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。

同时最后至于警报信息具体发给谁，满足什么样的条件下指定警报接收人，设置不同报警发送频率，

这里有 alertmanager 的 route 路由规则进行配置。

#创建 prometheus 和告警规则配置文件

在 k8s 的控制节点生成一个 prometheus-alertmanager-cfg.yaml 文件并上传到 k8s 的 master1 节点

[root@master1 prometheus]# cat > /root/prometheus/prometheus-alertmanager-cfg.yaml <<END
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:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:10249','192.168.1.181:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$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}}%)"
END
[root@master1 prometheus]# kubectl delete -f prometheus-cfg.yaml 
configmap "prometheus-config" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-cfg.yaml 
configmap/prometheus-config created
[root@master1 prometheus]# kubectl get cm -n monitor-sa
NAME
DATA
AGE
alertmanager
1
75m
kube-root-ca.crt
1
2d20h
prometheus-config
2
5s

安装 prometheus 和 alertmanager

需要把 alertmanager.tar.gz 镜像包上传的 k8s 的各个工作节点,这个环境仅有node1，手动解压：

[root@node1 prometheus]# ls alertmanager.tar.gz 
alertmanager.tar.gz
[root@node1 prometheus]# du -sh alertmanager.tar.gz 
32M
alertmanager.tar.gz
[root@node1 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

在 k8s 的控制节点master1生成一个 prometheus-alertmanager-deploy.yaml

[root@master1 prometheus]# cat > /root/prometheus/prometheus-alertmanager-deploy.yaml <<END 
---
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: localtime
mountPath: /etc/localtime
- 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
END
#生成一个 etcd-certs，这个在部署 prometheus 需要
[root@master1 prometheus]# kubectl create secret generic etcd-certs -n monitor-sa --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
You have new mail in /var/spool/mail/root
[root@master1 prometheus]# kubectl get secret -n monitor-sa
NAME
TYPE
DATA
AGE
default-token-78mw6
kubernetes.io/service-account-token
3
2d20h
etcd-certs
Opaque
3
24s
monitor-token-gbxmj
kubernetes.io/service-account-token
3
2d19h
[root@master1 prometheus]# kubectl describe secret etcd-certs -n monitor-sa
Name:
etcd-certs
Namespace:
monitor-sa
Labels:
<none>
Annotations:
<none>
Type:
Opaque
Data
====
server.key:
1679 bytes
ca.crt:
1058 bytes
server.crt:
1176 bytes

通过 kubectl apply 更新资源清单 yaml 文件

[root@master1 prometheus]# kubectl get pods -n monitor-sa
NAME
READY
STATUS
RESTARTS
AGE
node-exporter-92k4d
1/1
Running
0
2d20h
node-exporter-d44k4
1/1
Running
0
2d20h
prometheus-server-657bd8cb4d-zrmk4
1/1
Running
0
2d15h
[root@master1 prometheus]# kubectl delete -f prometheus-deploy.yaml 
deployment.apps "prometheus-server" deleted
[root@master1 prometheus]# kubectl get pods -n monitor-sa
NAME
READY
STATUS
RESTARTS
AGE
node-exporter-92k4d
1/1
Running
0
2d20h
node-exporter-d44k4
1/1
Running
0
2d20h
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml 
deployment.apps/prometheus-server created
[root@master1 prometheus]# kubectl get deploy -n monitor-sa
NAME
READY
UP-TO-DATE
AVAILABLE
AGE
prometheus-server
1/1
1
1
6s
[root@master1 prometheus]# kubectl get rs -n monitor-sa
NAME
DESIRED
CURRENT
READY
AGE
prometheus-server-55cd9cb6d7
1
1
1
24s
[root@master1 prometheus]# kubectl get pods -n monitor-sa -o wide
NAME
READY
STATUS
RESTARTS
AGE
IP
NODE
NOMINATED NODE
READINESS GATES
node-exporter-92k4d
1/1
Running
0
2d20h
192.168.1.181
node1
<none>
<none>
node-exporter-d44k4
1/1
Running
0
2d20h
192.168.1.180
master1
<none>
<none>
prometheus-server-55cd9cb6d7-v7rwh
2/2
Running
0
32s
10.244.166.188
node1
<none>
<none>

部署 alertmanager 的 service，方便在浏览器访问

在 k8s 的控制节点生成一个 alertmanager-svc.yaml 文件

[root@master1 prometheus]# cat > /root/prometheus/alertmanager-svc.yaml <<END
---
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
END
[root@master1 prometheus]# ls alertmanager-svc.yaml 
alertmanager-svc.yaml
[root@master1 prometheus]# du -sh alertmanager-svc.yaml
4.0K
alertmanager-svc.yaml
[root@master1 prometheus]# kubectl apply -f alertmanager-svc.yaml 
service/alertmanager created
You have new mail in /var/spool/mail/root
[root@master1 prometheus]# kubectl get svc -n monitor-sa
NAME
TYPE
CLUSTER-IP
EXTERNAL-IP
PORT(S)
AGE
alertmanager
NodePort
10.110.81.137
<none>
9093:30066/TCP
16s
prometheus
NodePort
10.103.238.66
<none>
9090:31935/TCP
2d16h

使用master1的IP访问alertmanager UI

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访问 prometheus 的 web 界面

点击 status->targets，可看到如下

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从上面可以发现 kubernetes-controller-manager 和 kubernetes-schedule 都显示连接不上对应的端

口

可按如下方法处理：

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因为kube-proxy是受控制器管理的，所以删除后会自动重建
kubectl get pods -n kube-system|grep kube-proxy|awk '{print $1}'|xargs kubectl delete pods -n kube-system

可以看到相应的端口已经被物理机监听了

检查邮箱发现已收到邮件报警

扩展：暴力更新配置文件

修改 prometheus 任何一个配置文件之后，可通过 kubectl apply 使配置生效，执行顺序如下：

kubectl delete -f alertmanager-cm.yaml

kubectl apply -f alertmanager-cm.yaml

kubectl delete -f prometheus-alertmanager-cfg.yaml

kubectl apply -f prometheus-alertmanager-cfg.yaml

kubectl delete -f prometheus-alertmanager-deploy.yaml

kubectl apply -f prometheus-alertmanager-deploy.yaml

6. 配置 alertmanager-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

https://developers.dingtalk.com/document/app/custom-robot-access

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：test

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 test 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 test 这个机器人，点击 test，就会进入到 test 机器人的设

置界面

出现如下内容：

机器人名称：test

接受群组：钉钉报警测试

消息推送：开启

webhook：

这个每个人得到的不一样，复制备用

安全设置：

自定义关键词：cluster1

安装钉钉的 webhook 插件，在 k8s 的控制节点 master1 操作

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/1bxkiE83Nv5dEvLB1ZldEcw
提取码：ndm4

tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz

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=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx” &

这里的xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx是前面复制的webhook

对原来的 alertmanager-cm.yaml 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

[root@master1 prometheus]# cat > /root/prometheus/alertmanager-cm.yaml <<END
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.qq.com:465'
smtp_from: 'xxxxx@qq.com'
smtp_auth_username: 'xxxxx'
smtp_auth_password: 'xxxxxxxxx'
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://192.168.1.180:8060/dingtalk/cluster1/send'
send_resolved: true
END
[root@master1 prometheus]# kubectl delete -f alertmanager-cm.yaml
configmap "alertmanager" deleted
[root@master1 prometheus]# kubectl apply -f alertmanager-cm.yaml
configmap/alertmanager created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-cfg.yaml 
configmap "prometheus-config" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-cfg.yaml 
configmap/prometheus-config created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-deploy.yaml 
deployment.apps "prometheus-server" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml 
deployment.apps/prometheus-server created

7. 配置 alertmanager-发送报警到微信

注册企业微信

登陆网址：

https://work.weixin.qq.com/

找到应用管理，创建应用

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应用名字 wechat

创建成功之后显示如下：

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[root@master1 prometheus]# cat > /root/prometheus/alertmanager-cm.yaml <<END
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.qq.com:465'
smtp_from: 'xxxxx@qq.com'
smtp_auth_username: 'xxxxx'
smtp_auth_password: 'xxxxxxx'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: prometheus
receivers:
- name: 'prometheus'
wechat_configs:
- corp_id: ww7xxxxxx
#换成你自已的
to_user: '@all'
agent_id: 1000003
api_secret: xxxxxxxxxxxxxxxxxxxx
#换成你自已的
END
[root@master1 prometheus]# kubectl delete -f alertmanager-cm.yaml
configmap "alertmanager" deleted
[root@master1 prometheus]# kubectl apply -f alertmanager-cm.yaml
configmap/alertmanager created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-deploy.yaml 
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml 
deployment.apps/prometheus-server created
[root@master1 prometheus]# kubectl get pods -n monitor-sa
NAME
READY
STATUS
RESTARTS
AGE
node-exporter-92k4d
1/1
Running
0
3d5h
node-exporter-d44k4
1/1
Running
2
3d5h
prometheus-server-55cd9cb6d7-6pj7z
2/2
Running
0
28s

发送成功

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8. Prometheus 监控扩展

8.1 promethues 采集 tomcat 监控数据

tomcat_exporter地址

链接：https://pan.baidu.com/s/1E2nDbVX3VcRxTxxowVaNtQ
提取码：nrml

下面在k8s-node节点操作

（1）制作tomcat镜像，按如下步骤

mkdir /root/tomcat_image

把上面的war包和jar包传到这个目录下

cd /root/tomcat_image
cat > Dockerfile <<END
FROM tomcat
ADD metrics.war /usr/local/tomcat/webapps/
ADD simpleclient-0.8.0.jar
/usr/local/tomcat/lib/
ADD simpleclient_common-0.8.0.jar /usr/local/tomcat/lib/
ADD simpleclient_hotspot-0.8.0.jar /usr/local/tomcat/lib/
ADD simpleclient_servlet-0.8.0.jar /usr/local/tomcat/lib/
ADD tomcat_exporter_client-0.0.12.jar /usr/local/tomcat/lib/
END
[root@node1 tomcat_image]# docker build -t='mack/tomcat_prometheus:v1' .
[root@node1 tomcat_image]# docker images
REPOSITORY
TAG
IMAGE ID
CREATED
SIZE
mack/tomcat_prometheus
v1
d60e7a86371f
7 minutes ago
680MB

基于上面的镜像创建一个tomcat实例

下面操作在master1节点进行

cat > /root/tomcat_deploy.yaml <<END
apiVersion: apps/v1
kind: Deployment
metadata:
name: tomcat-deployment
namespace: default
spec:
selector:
matchLabels:
app: tomcat
replicas: 2 # tells deployment to run 2 pods matching the template
template: # create pods using pod definition in this template
metadata:
labels:
app: tomcat
annotations:
prometheus.io/scrape: 'true'
spec:
containers:
- name: tomcat
image: mack/tomcat_prometheus:v1
imagePullPolicy: IfNotPresent
ports:
- containerPort: 8080
securityContext:
privileged: true
END
kubectl apply -f tomcat_deploy.yaml
deployment.apps/tomcat-deployment created

创建一个service，可操作也可不操作
cat > /root/tomcat-service.yaml <<END
kind: Service
#service 类型
apiVersion: v1
metadata:
#
annotations:
#
prometheus.io/scrape: 'true'
name: tomcat-service
spec:
selector:
app: tomcat
ports:
- nodePort: 31360
port: 80
protocol: TCP
targetPort: 8080
type: NodePort
END
kubectl apply -f tomcat-service.yaml

在promethues上可以看到监控到tomcat的pod了

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8.2 Prometheus 监控 mysql

#master1
[root@master1 ~]# yum install mysql -y
[root@master1 ~]# yum install mariadb* -y
[root@master1 ~]# systemctl start mariadb
[root@master1 ~]# ls mysqld_exporter-0.10.0.linux-amd64.tar.gz 
mysqld_exporter-0.10.0.linux-amd64.tar.gz
[root@master1 ~]# du -sh mysqld_exporter-0.10.0.linux-amd64.tar.gz
3.3M
mysqld_exporter-0.10.0.linux-amd64.tar.gz
[root@master1 ~]# tar -zxvf mysqld_exporter-0.10.0.linux-amd64.tar.gz
mysqld_exporter-0.10.0.linux-amd64/
mysqld_exporter-0.10.0.linux-amd64/LICENSE
mysqld_exporter-0.10.0.linux-amd64/NOTICE
mysqld_exporter-0.10.0.linux-amd64/mysqld_exporter
[root@master1 ~]# 
cd mysqld_exporter-0.10.0.linux-amd64
cp -ar mysqld_exporter /usr/local/bin/
[root@master1 mysqld_exporter-0.10.0.linux-amd64]# cd /usr/local/bin/
[root@master1 bin]# ll
total 10176
-rwxr-xr-x 1 1000 1000 10419174 Apr 25
2017 mysqld_exporter

登陆 mysql 为 mysql_exporter 创建账号并授权

创建数据库用户。

mysql

CREATE USER ‘mysql_exporter’@‘localhost’ IDENTIFIED BY ‘Abcdef123!.’;

对 mysql_exporter 用户授权

mysql

GRANT PROCESS, REPLICATION CLIENT, SELECT ON . TO ‘mysql_exporter’@‘localhost’;

exit 退出 mysql

创建 mysql 配置文件、运行时可免密码连接数据库：

cd mysqld_exporter-0.10.0.linux-amd64
cat > my.cnf <<END
[client]
user=mysql_exporter
password=Abcdef123!.
END

启动 mysql_exporter 客户端

nohup ./mysqld_exporter --config.my-cnf=./my.cnf &

mysqld_exporter 的监听端口是 9104

修改 prometheus-alertmanager-cfg.yaml 文件，添加如下

- job_name: 'mysql'
static_configs:
- targets: ['192.168.40.180:9104']

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[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-cfg.yaml
configmap "prometheus-config" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-cfg.yaml
configmap/prometheus-config created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-deploy.yaml
deployment.apps "prometheus-server" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml
deployment.apps/prometheus-server created

grafana 导入 mysql 监控图表

mysql-overview_rev5.json

8.3 Prometheus 监控 Nginx

所需要的文件下载地址

链接：https://pan.baidu.com/s/1SD1TPFNjBnf9wLIfiQVqHg
提取码：uf3f

#1,在master1上下载nginx-module-vts模块
[root@master1 prometheus]# ls nginx-module-vts-master.zip 
nginx-module-vts-master.zip
[root@master1 prometheus]# du -sh nginx-module-vts-master.zip 
400K
nginx-module-vts-master.zip
[root@master1 prometheus]# unzip nginx-module-vts-master.zip 
[root@master1 prometheus]# mv nginx-module-vts-master /usr/local/
#2,安装nginx
[root@master1 prometheus]# ls nginx-1.15.7.tar.gz 
nginx-1.15.7.tar.gz
[root@master1 prometheus]# du -sh nginx-1.15.7.tar.gz
1004K
nginx-1.15.7.tar.gz
[root@master1 nginx-1.15.7]# cd nginx-1.15.7/
[root@master1 nginx-1.15.7]# ./configure
--prefix=/usr/local/nginx --with-http_gzip_static_module --with-http_stub_status_module --with-http_ssl_module --with-pcre --with-file-aio --with-http_realip_module --add-module=/usr/local/nginx-module-vts-master
[root@master1 nginx-1.15.7]# make && make install
修改nginx配置文件：
vim /usr/local/nginx/conf/nginx.conf
#server下添加如下：
location /status {
vhost_traffic_status_display;
vhost_traffic_status_display_format html;
}
# http中添加如下：

vhost_traffic_status_zone;
#测试nginx配置文件是否正确：
[root@master1 nginx-1.15.7]# /usr/local/nginx/sbin/nginx -t
nginx: the configuration file /usr/local/nginx/conf/nginx.conf syntax is ok
nginx: configuration file /usr/local/nginx/conf/nginx.conf test is successful
#如果正确没问题，启动nginx
#启动nginx：
[root@master1 nginx-1.15.7]# /usr/local/nginx/sbin/nginx
#访问192.168.1.180/status可以看到nginx监控数据

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#3,安装nginx-vts-exporter
[root@master1 prometheus]# ls nginx-vts-exporter-0.5.zip 
nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# du -sh nginx-vts-exporter-0.5.zip
3.2M
nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# unzip nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# mv nginx-vts-exporter-0.5 /usr/local/
[root@master1 prometheus]# cd /usr/local/nginx-vts-exporter-0.5/bin/
[root@master1 bin]# ls -l
total 8932
-rw-r--r-- 1 root root 9145803 Mar 16
2017 nginx-vts-exporter
[root@master1 bin]# chmod a+x nginx-vts-exporter 
You have new mail in /var/spool/mail/root
[root@master1 bin]# ls -l
total 8932
-rwxr-xr-x 1 root root 9145803 Mar 16
2017 nginx-vts-exporter
[root@master1 bin]# nohup ./nginx-vts-exporter -nginx.scrape_uri http://192.168.1.180/status/format/json &
[3] 17102
You have new mail in /var/spool/mail/root
[root@master1 bin]# nohup: ignoring input and appending output to ‘nohup.out’
[root@master1 bin]# 
[root@master1 bin]# cat nohup.out 
2022/06/13 15:31:46 Starting nginx_vts_exporter (version=0.4, branch=fix-docker-error, revision=0f3dbb44a86340d65bf3d6abbcc0ee88663cb419)
2022/06/13 15:31:46 Build context (go=go1.8, user=Administrator@LS--20151110SAS, date=20170316-03:16:26)
2022/06/13 15:31:46 Starting Server at : :9913
2022/06/13 15:31:46 Metrics endpoint: /metrics
2022/06/13 15:31:46 Metrics namespace: nginx
2022/06/13 15:31:46 Scraping information from : http://192.168.1.180/status/format/json
#nginx-vts-exporter的监听端口是9913
#4 修改prometheus-alertmanager-cfg.yaml文件
添加如下job
- job_name: 'nginx'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:9913']
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-cfg.yaml 
configmap "prometheus-config" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-cfg.yaml
configmap/prometheus-config created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-deploy.yaml 
deployment.apps "prometheus-server" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml
deployment.apps/prometheus-server created
#5 在grafana界面导入nginx json

nfiguration file /usr/local/nginx/conf/nginx.conf test is successful

#如果正确没问题，启动nginx
#启动nginx：
[root@master1 nginx-1.15.7]# /usr/local/nginx/sbin/nginx

#访问192.168.1.180/status可以看到nginx监控数据

[外链图片转存中...(img-16Flps63-1655106671771)]
```bash
#3,安装nginx-vts-exporter
[root@master1 prometheus]# ls nginx-vts-exporter-0.5.zip
nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# du -sh nginx-vts-exporter-0.5.zip
3.2M
nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# unzip nginx-vts-exporter-0.5.zip
[root@master1 prometheus]# mv nginx-vts-exporter-0.5 /usr/local/
[root@master1 prometheus]# cd /usr/local/nginx-vts-exporter-0.5/bin/
[root@master1 bin]# ls -l
total 8932
-rw-r--r-- 1 root root 9145803 Mar 16
2017 nginx-vts-exporter
[root@master1 bin]# chmod a+x nginx-vts-exporter
You have new mail in /var/spool/mail/root
[root@master1 bin]# ls -l
total 8932
-rwxr-xr-x 1 root root 9145803 Mar 16
2017 nginx-vts-exporter
[root@master1 bin]# nohup ./nginx-vts-exporter -nginx.scrape_uri http://192.168.1.180/status/format/json &
[3] 17102
You have new mail in /var/spool/mail/root
[root@master1 bin]# nohup: ignoring input and appending output to ‘nohup.out’
[root@master1 bin]#
[root@master1 bin]# cat nohup.out
2022/06/13 15:31:46 Starting nginx_vts_exporter (version=0.4, branch=fix-docker-error, revision=0f3dbb44a86340d65bf3d6abbcc0ee88663cb419)
2022/06/13 15:31:46 Build context (go=go1.8, user=Administrator@LS--20151110SAS, date=20170316-03:16:26)
2022/06/13 15:31:46 Starting Server at : :9913
2022/06/13 15:31:46 Metrics endpoint: /metrics
2022/06/13 15:31:46 Metrics namespace: nginx
2022/06/13 15:31:46 Scraping information from : http://192.168.1.180/status/format/json
#nginx-vts-exporter的监听端口是9913
#4 修改prometheus-alertmanager-cfg.yaml文件
添加如下job
- job_name: 'nginx'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.180:9913']
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-cfg.yaml
configmap "prometheus-config" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-cfg.yaml
configmap/prometheus-config created
[root@master1 prometheus]# kubectl delete -f prometheus-alertmanager-deploy.yaml
deployment.apps "prometheus-server" deleted
[root@master1 prometheus]# kubectl apply -f prometheus-alertmanager-deploy.yaml
deployment.apps/prometheus-server created
#5 在grafana界面导入nginx json

最后

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