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前言

本篇文章介绍k8s集群中部署prometheus、grafana、alertmanager,并且配置prometheus的动态、静态服务发现,实现对容器、物理节点、service、pod等资源指标监控,并在Grafana的web界面展示prometheus的监控指标,然后通过配置自定义告警规则,通过alertmanager实现qq、钉钉、微信报警,文章内容较多,大概1.5万以上字数,可以先关注和转发,在慢慢学习。

prometheus简介

Prometheus是一个开源的系统监控和报警系统,现在已经加入到CNCF基金会,成为继k8s之后第二个在CNCF托管的项目,在kubernetes容器管理系统中,通常会搭配prometheus进行监控,同时也支持多种exporter采集数据,还支持pushgateway进行数据上报,Prometheus性能足够支撑上万台规模的集群。

prometheus特点

1.多维度数据模型

时间序列数据由metrics名称和键值对来组成
可以对数据进行聚合,切割等操作
所有的metrics都可以设置任意的多维标签。

2.灵活的查询语言(PromQL)

可以对采集的metrics指标进行加法,乘法,连接等操作;

3.可以直接在本地部署,不依赖其他分布式存储;

4.通过基于HTTP的pull方式采集时序数据;

5.可以通过中间网关pushgateway的方式把时间序列数据推送到prometheus server端;

6.可通过服务发现或者静态配置来发现目标服务对象(targets)。

7.有多种可视化图像界面,如Grafana等。

8.高效的存储,每个采样数据占3.5 bytes左右,300万的时间序列,30s间隔,保留60天,消耗磁盘大概200G。

prometheus组件介绍

1.Prometheus Server​: 用于收集和存储时间序列数据。

2.Client Library: 客户端库,检测应用程序代码,当Prometheus抓取实例的HTTP端点时,客户端库会将所有跟踪的metrics指标的当前状态发送到prometheus server端。

3.Exporters​: prometheus支持多种exporter,通过exporter可以采集metrics数据,然后发送到prometheus server端

4.Alertmanager​: 从 Prometheus server 端接收到 alerts 后,会进行去重,分组,并路由到相应的接收方,发出报警,常见的接收方式有:电子邮件,微信,钉钉, slack等。

5.Grafana​监控仪表盘

6.pushgateway​: 各个目标主机可上报数据到pushgatewy,然后prometheus server统一从pushgateway拉取数据。

prometheus架构图

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_数据

从上图可发现,Prometheus整个生态圈组成主要包括prometheus server,Exporter,pushgateway,alertmanager,grafana,Web ui界面,Prometheus server由三个部分组成,Retrieval,Storage,PromQL

Retrieval负责在活跃的target主机上抓取监控指标数据
Storage存储主要是把采集到的数据存储到磁盘中
PromQL是Prometheus提供的查询语言模块。

prometheus工作流程:

1.  Prometheus  server可定期从活跃的(up)目标主机上(target)拉取监控指标数据,目标主机的监控数据可通过配置静态job或者服务发现的方式被prometheus server采集到,这种方式默认的pull方式拉取指标;也可通过pushgateway把采集的数据上报到prometheus server中;还可通过一些组件自带的exporter采集相应组件的数据;

2.Prometheus server把采集到的监控指标数据保存到本地磁盘或者数据库;

3.Prometheus采集的监控指标数据按时间序列存储,通过配置报警规则,把触发的报警发送到alertmanager

4.Alertmanager通过配置报警接收方,发送报警到邮件,微信或者钉钉等

5.Prometheus 自带的web ui界面提供PromQL查询语言,可查询监控数据

6.Grafana可接入prometheus数据源,把监控数据以图形化形式展示出

安装node-exporter组件

机器规划:

我的实验环境使用的k8s集群是一个master节点和一个node节点

master节点的机器ip是192.168.0.6,主机名是master1

node节点的机器ip是192.168.0.56,主机名是node1

master高可用集群安装可参考如下文章:

​k8s1.18高可用集群安装-超详细中文官方文档​

​k8s1.18多master节点高可用集群安装-超详细中文官方文档​

node-exporter是什么?

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。

安装node-exporter组件,在k8s集群的master1节点操作

cat >node-export.yaml  <<EOF
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: /
EOF

#通过kubectl apply更新node-exporter

kubectl apply -f node-export.yaml

#查看node-exporter是否部署成功

 kubectl get pods -n monitor-sa

显示如下,看到pod的状态都是running,说明部署成功

NAME                  READY   STATUS    RESTARTS   AGE
node-exporter-9qpkd 1/1 Running 0 89s
node-exporter-zqmnk 1/1 Running 0 89s

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,截取一部分,如下

# 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"} 56136.98
# HELP node_load1 1m load average.
# TYPE node_load1 gauge
node_load1 0.58


#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_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)


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
gauge标准尺寸:统计的指标可增加可减少

k8s集群中部署prometheus

1.创建namespace、sa账号​,​在k8s集群的master节点操作

#创建一个monitor-sa的名称空间

kubectl create ns monitor-sa 

#创建一个sa账号

kubectl create serviceaccount monitor -n monitor-sa  

#把sa账号monitor通过clusterrolebing绑定到clusterrole上

kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin  --serviceaccount=monitor-sa:monitor

2.创建数据目录

#在k8s集群的任何一个node节点操作,因为我的k8s集群只有一个node节点node1,所以我在node1上操作如下命令:

mkdir /data

chmod 777 /data/

3.安装prometheus,以下步骤均在在k8s集群的master1节点操作

1)创建一个configmap存储卷,用来存放prometheus配置信息

cat  >prometheus-cfg.yaml <<EOF
---
kind: ConfigMap
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus-config
namespace: monitor-sa
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
EOF

注意:​通过上面命令生成的promtheus-cfg.yaml文件会有一些问题,$1和$2这种变量在文件里没有,需要在k8s的master1节点打开promtheus-cfg.yaml文件,手动把$1和$2这种变量写进文件里,promtheus-cfg.yaml文件需要手动修改部分如下:

22行的replacement: ':9100'变成replacement: '${1}:9100'
42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
73行的replacement: 变成replacement: $1:$2

#通过kubectl apply更新configmap

kubectl apply  -f  prometheus-cfg.yaml

2)通过deployment部署prometheus

cat  >prometheus-deploy.yaml <<EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
component: server
#matchExpressions:
#- {key: app, operator: In, values: [prometheus]}
#- {key: component, operator: In, values: [server]}
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
nodeName: node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
imagePullPolicy: IfNotPresent
command:
- prometheus
- --config.file=/etc/prometheus/prometheus.yml
- --storage.tsdb.path=/prometheus
- --storage.tsdb.retention=720h
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
EOF

注意:​在上面的prometheus-deploy.yaml文件有个​nodeName​字段,这个就是用来指定创建的这个prometheus的pod调度到哪个节点上,我们这里让nodeName=node1,也即是让pod调度到node1节点上,因为node1节点我们创建了数据目录/data,所以大家记住:你在k8s集群的哪个节点创建/data,就让pod调度到哪个节点。

#通过kubectl apply更新prometheus

kubectl apply -f prometheus-deploy.yaml

#查看prometheus是否部署成功

kubectl get pods -n monitor-sa

显示如下,可看到pod状态是running,说明prometheus部署成功

NAME                                 READY   STATUS    RESTARTS   AGE
node-exporter-9qpkd 1/1 Running 0 76m
node-exporter-zqmnk 1/1 Running 0 76m
prometheus-server-85dbc6c7f7-nsg94 1/1 Running 0 6m7

3)给prometheus pod创建一个service

cat  > prometheus-svc.yaml << EOF
---
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
EOF

#通过kubectl apply 更新service

kubectl  apply -f prometheus-svc.yaml

#查看service在物理机映射的端口

kubectl get svc -n monitor-sa

显示如下:

NAME         TYPE       CLUSTER-IP    EXTERNAL-IP   PORT(S)          AGE
prometheus NodePort 10.96.45.93 <none> 9090:31043/TCP 50s

通过上面可以看到service在宿主机上映射的端口是31043,这样我们访问k8s集群的master1节点的ip:31043,就可以访问到prometheus的web ui界面了

#访问prometheus web ui界面

火狐浏览器输入如下地址

​​

可看到如下页面:

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_02

#点击页面的Status->Targets,可看到如下,说明我们配置的服务发现可以正常采集数据Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_数据_03

prometheus热更新

#为了每次修改配置文件可以热加载prometheus,也就是不停止prometheus,就可以使配置生效,如修改prometheus-cfg.yaml,想要使配置生效可用如下热加载命令:

curl -X POST http://10.244.1.66:9090/-/reload

#10.244.1.66是prometheus的pod的ip地址,如何查看prometheus的pod ip,可用如下命令:

kubectl get pods -n monitor-sa -o wide | grep prometheus

显示如下, 10.244.1.7就是prometheus的ip

prometheus-server-85dbc6c7f7-nsg94   1/1     Running   0          29m   10.244.1.7     node1     <none>           <none>

#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

Grafana安装和配置

下载安装Grafana需要的镜像

上传heapster-grafana-amd64_v5_0_4.tar.gz镜像到k8s的各个master节点和k8s的各个node节点,然后在各个节点手动解压:

docker load -i heapster-grafana-amd64_v5_0_4.tar.gz

镜像所在的百度网盘地址如下:

链接:https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 
提取码:052u

在k8s的master1节点创建grafana.yaml

cat  >grafana.yaml <<  EOF
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
EOF

通过kubectl apply 更新grafana

kubectl  apply -f grafana.yaml

查看grafana是否部署成功

kubectl get pods -n kube-system

显示如下,说明部署成功

monitoring-grafana-7d7f6cf5c6-vrxw9   1/1     Running   0          3h51m

查看grafana的service
​kubectl get svc -n kube-system

显示如下:

monitoring-grafana   NodePort    10.111.173.47    <none>        80:31044/TCP             3h54m

上面可以看到grafana暴露的宿主机端口是31044

我们访问k8s集群的master节点ip:31044即可访问到grafana的web界面

Grafan界面接入prometheus数据源

1)登陆grafana,在浏览器访问

192.168.0.6:31044

账号密码都是admin

可看到如下界面:

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_04

2)配置grafana界面:

开始配置grafana的web界面:

选择Create your first data source


Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_05

出现如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_06

Name: Prometheus 

Type: Prometheus

HTTP 处的URL写 如下:

​​

配置好的整体页面如下:

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_07

点击左下角Save & Test,出现如下Data source is working,说明prometheus数据源成功的被grafana接入了

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_08

导入监控模板,可在如下链接搜索

​​

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

node_exporter.json在百度网盘地址如下:

链接:https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 
提取码:kyd6

还可直接导入docker_rev1.json,可以把容器相关的数据展示出来

docker_rev1.json在百度网盘地址如下:

链接:https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 
提取码:vinv

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

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_09

点击左侧+号下面的Import,出现如下界面

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_10

选择Upload json file,出现如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_数据_11

选择一个本地的json文件,我们选择的是上面让大家下载的node_exporter.json这个文件,选择之后出现如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_12

注:箭头标注的地方Name后面的名字是node_exporter.json定义的

Prometheus后面需要变成Prometheus,然后再点击Import,就可以出现如下界面:

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_13

导入docker_rev1.json监控模板,步骤和上面导入node_exporter.json步骤一样,导入之后显示如下:

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_14

安装配置kube-state-metrics组件

kube-state-metrics是什么?

kube-state-metrics通过监听API Server生成有关资源对象的状态指标,比如Deployment、Node、Pod,需要注意的是kube-state-metrics只是简单的提供一个metrics数据,并不会存储这些指标数据,所以我们可以使用Prometheus来抓取这些数据然后存储,主要关注的是业务相关的一些元数据,比如Deployment、Pod、副本状态等;调度了多少个replicas?现在可用的有几个?多少个Pod是running/stopped/terminated状态?Pod重启了多少次?我有多少job在运行中。

安装kube-state-metrics组件

1)创建sa,并对sa授权

在k8s的master1节点生成一个kube-state-metrics-rbac.yaml文件

cat > kube-state-metrics-rbac.yaml <<EOF
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: kube-state-metrics
namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: kube-state-metrics
rules:
- apiGroups: [""]
resources: ["nodes", "pods", "services", "resourcequotas", "replicationcontrollers", "limitranges", "persistentvolumeclaims", "persistentvolumes", "namespaces", "endpoints"]
verbs: ["list", "watch"]
- apiGroups: ["extensions"]
resources: ["daemonsets", "deployments", "replicasets"]
verbs: ["list", "watch"]
- apiGroups: ["apps"]
resources: ["statefulsets"]
verbs: ["list", "watch"]
- apiGroups: ["batch"]
resources: ["cronjobs", "jobs"]
verbs: ["list", "watch"]
- apiGroups: ["autoscaling"]
resources: ["horizontalpodautoscalers"]
verbs: ["list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: kube-state-metrics
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kube-state-metrics
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: kube-system
EOF

通过kubectl apply更新yaml文件

kubectl apply -f kube-state-metrics-rbac.yaml

2)安装kube-state-metrics组件

在k8s的master1节点生成一个kube-state-metrics-deploy.yaml文件

cat > kube-state-metrics-deploy.yaml <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
name: kube-state-metrics
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
app: kube-state-metrics
template:
metadata:
labels:
app: kube-state-metrics
spec:
serviceAccountName: kube-state-metrics
containers:
- name: kube-state-metrics
# image: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1
image: quay.io/coreos/kube-state-metrics:v1.9.0
ports:
- containerPort: 8080
EOF

通过kubectl apply更新yaml文件

kubectl apply -f kube-state-metrics-deploy.yaml

查看kube-state-metrics是否部署成功

kubectl get pods -n kube-system

显示如下,看到pod处于running状态,说明部署成功

kube-state-metrics-79c9686b96-4njrs   1/1     Running   0          76s

3)创建service
在8s的master1节点生成一个kube-state-metrics-svc.yaml文件

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

通过kubectl apply更新yaml

kubectl apply -f kube-state-metrics-svc.yaml

查看service是否创建成功

kubectl get svc -n kube-system | grep kube-state-metrics

显示如下,说明创建成功

kube-state-metrics   ClusterIP   10.105.53.102    <none>        8080/TCP                 2m38s

在grafana web界面导入Kubernetes Cluster (Prometheus)-1577674936972.json,出现如下页面

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_15

在grafana web界面导入Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json,出现如下页面

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_16

Kubernetes Cluster (Prometheus)-1577674936972.json和Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json文件在百度网盘,地址如下:

链接:https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA 
提取码:i4yd

安装和配置Alertmanager-发送报警到qq邮箱

在k8s的master1节点创建alertmanager-cm.yaml文件

cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.163.com:25'
smtp_from: '15011572657@163.com'
smtp_auth_username: '15011572657'
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: default-receiver
receivers:
- name: 'default-receiver'
email_configs:
- to: '1980570647@qq.com'
send_resolved: true
EOF

通过kubectl apply 更新文件

kubectl apply -f alertmanager-cm.yaml

alertmanager配置文件解释说明:

smtp_smarthost: 'smtp.163.com:25'
#用于发送邮件的邮箱的SMTP服务器地址+端口
smtp_from: '15011572657@163.com'
#这是指定从哪个邮箱发送报警
smtp_auth_username: '15011572657'
#这是发送邮箱的认证用户,不是邮箱名
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
#这是发送邮箱的授权码而不是登录密码
email_configs:
- to: '1980570647@qq.com'
#to后面指定发送到哪个邮箱,我发送到我的qq邮箱,大家需要写自己的邮箱地址,不应该跟smtp_from的邮箱名字重复

在k8s的master1节点重新生成一个prometheus-cfg.yaml文件

cat prometheus-cfg.yaml

kind: ConfigMap
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus-config
namespace: monitor-sa
data:
prometheus.yml: |
rule_files:
- /etc/prometheus/rules.yml
alerting:
alertmanagers:
- static_configs:
- targets: ["localhost:9093"]
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 1m
scrape_configs:
- job_name: 'kubernetes-node'
kubernetes_sd_configs:
- role: node
relabel_configs:
- source_labels: [__address__]
regex: '(.*):10250'
replacement: '${1}:9100'
target_label: __address__
action: replace
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- job_name: 'kubernetes-node-cadvisor'
kubernetes_sd_configs:
- role: node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
- job_name: 'kubernetes-apiserver'
kubernetes_sd_configs:
- role: endpoints
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
action: keep
regex: default;kubernetes;https
- job_name: 'kubernetes-service-endpoints'
kubernetes_sd_configs:
- role: endpoints
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
action: replace
target_label: __address__
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
- job_name: kubernetes-pods
kubernetes_sd_configs:
- role: pod
relabel_configs:
- action: keep
regex: true
source_labels:
- __meta_kubernetes_pod_annotation_prometheus_io_scrape
- action: replace
regex: (.+)
source_labels:
- __meta_kubernetes_pod_annotation_prometheus_io_path
target_label: __metrics_path__
- action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['192.168.0.6:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['192.168.0.6:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['192.168.0.6:10249','192.168.0.56:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['192.168.0.6:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: kube-proxy的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: scheduler的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: scheduler的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: controller-manager的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: controller-manager的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: apiserver的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: apiserver的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: etcd的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: etcd的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: kube-state-metrics的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: kube-state-metrics的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: coredns的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: coredns的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: kube-proxy打开句柄数>600
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kube-proxy打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>600
expr: process_open_fds{job=~"kubernetes-schedule"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-schedule"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>600
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>600
expr: process_open_fds{job=~"kubernetes-apiserver"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-apiserver"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>600
expr: process_open_fds{job=~"kubernetes-etcd"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-etcd"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"
value: "{{ $value }}"
- alert: kube-proxy
expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: scheduler
expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-controller-manager
expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-apiserver
expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-etcd
expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kube-dns
expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: HttpRequestsAvg
expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m])) > 1000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"
value: "{{ $value }}"
threshold: "1000"
- alert: Pod_restarts
expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
for: 2s
labels:
severity: warnning
annotations:
description: "在{{$labels.namespace}}名称空间下发现{{$labels.pod}}这个pod下的容器{{$labels.container}}被重启,这个监控指标是由{{$labels.instance}}采集的"
value: "{{ $value }}"
threshold: "0"
- alert: Pod_waiting
expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"
value: "{{ $value }}"
threshold: "1"
- alert: Pod_terminated
expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"
value: "{{ $value }}"
threshold: "1"
- alert: Etcd_leader
expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_leader_changes
expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_failed
expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_db_total_size
expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}):db空间超过10G"
value: "{{ $value }}"
threshold: "10G"
- alert: Endpoint_ready
expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.endpoint}}不可用"
value: "{{ $value }}"
threshold: "1"
- name: 物理节点状态-监控告警
rules:
- alert: 物理节点cpu使用率
expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90
for: 2s
labels:
severity: ccritical
annotations:
summary: "{{ $labels.instance }}cpu使用率过高"
description: "{{ $labels.instance }}的cpu使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: 物理节点内存使用率
expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}内存使用率过高"
description: "{{ $labels.instance }}的内存使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: InstanceDown
expr: up == 0
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}: 服务器宕机"
description: "{{ $labels.instance }}: 服务器延时超过2分钟"
- alert: 物理节点磁盘的IO性能
expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入磁盘IO使用率过高!"
description: "{{$labels.mountpoint }} 流入磁盘IO大于60%(目前使用:{{$value}})"
- alert: 入网流量带宽
expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入网络带宽过高!"
description: "{{$labels.mountpoint }}流入网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: 出网流量带宽
expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流出网络带宽过高!"
description: "{{$labels.mountpoint }}流出网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: TCP会话
expr: node_netstat_Tcp_CurrEstab > 1000
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高!"
description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"
- alert: 磁盘容量
expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 磁盘分区使用率过高!"
description: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$value}}%)"

注意:​通过上面命令生成的promtheus-cfg.yaml文件会有一些问题,$1和$2这种变量在文件里没有,需要在k8s的master1节点打开promtheus-cfg.yaml文件,手动把$1和$2这种变量写进文件里,promtheus-cfg.yaml文件需要手动修改部分如下:

22行的replacement: ':9100'变成replacement: '${1}:9100'
42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
73行的replacement: 变成replacement: $1:$2
103行的replacement: 变成replacement: $1:$2

通过kubectl apply 更新文件

kubectl apply -f prometheus-cfg.yaml

在k8s的master1节点重新生成一个prometheus-deploy.yaml文件

cat >prometheus-deploy.yaml <<EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
component: server
#matchExpressions:
#- {key: app, operator: In, values: [prometheus]}
#- {key: component, operator: In, values: [server]}
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
nodeName: node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
imagePullPolicy: IfNotPresent
command:
- "/bin/prometheus"
args:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention=24h"
- "--web.enable-lifecycle"
ports:
- containerPort: 9090
protocol: TCP
volumeMounts:
- mountPath: /etc/prometheus
name: prometheus-config
- mountPath: /prometheus/
name: prometheus-storage-volume
- name: k8s-certs
mountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/
- name: alertmanager
image: prom/alertmanager:v0.14.0
imagePullPolicy: IfNotPresent
args:
- "--config.file=/etc/alertmanager/alertmanager.yml"
- "--log.level=debug"
ports:
- containerPort: 9093
protocol: TCP
name: alertmanager
volumeMounts:
- name: alertmanager-config
mountPath: /etc/alertmanager
- name: alertmanager-storage
mountPath: /alertmanager
- name: localtime
mountPath: /etc/localtime
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
- name: prometheus-storage-volume
hostPath:
path: /data
type: Directory
- name: k8s-certs
secret:
secretName: etcd-certs
- name: alertmanager-config
configMap:
name: alertmanager
- name: alertmanager-storage
hostPath:
path: /data/alertmanager
type: DirectoryOrCreate
- name: localtime
hostPath:
path: /usr/share/zoneinfo/Asia/Shanghai
EOF

生成一个etcd-certs,这个在部署prometheus需要

kubectl -n monitor-sa create secret generic etcd-certs --from-file=/etc/kubernetes/pki/etcd/server.key  --from-file=/etc/kubernetes/pki/etcd/server.crt --from-file=/etc/kubernetes/pki/etcd/ca.crt​
通过kubectl apply更新yaml文件

kubectl apply -f prometheus-deploy.yaml

#查看prometheus是否部署成功

kubectl get pods -n monitor-sa | grep prometheus

显示如下,可看到pod状态是running,说明prometheus部署成功

NAME                                 READY   STATUS    RESTARTS   AGE
prometheus-server-85dbc6c7f7-nsg94 1/1 Running 0 6m7

在k8s的master1节点重新生成一个alertmanager-svc.yaml文件

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

通过kubectl apply更新yaml文件

kubectl apply -f prometheus-svc.yaml

#查看service在物理机映射的端口

kubectl get svc -n monitor-sa

显示如下:

NAME           TYPE       CLUSTER-IP     EXTERNAL-IP   PORT(S)          AGE
alertmanager NodePort 10.111.49.65 <none> 9093:31043/TCP 25s
prometheus NodePort 10.96.45.93 <none> 9090:30090/TCP 34h

注意:上面可以看到prometheus的service暴漏的端口是31043,alertmanager的service暴露的端口是30066

访问prometheus的web界面

点击status->targets,可看到如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_17

点击Alerts,可看到如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_18

把controller-manager的cpu使用率大于90%展开,可看到如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_19

FIRING表示prometheus已经将告警发给alertmanager,在Alertmanager 中可以看到有一个 alert。

登录到alertmanager web界面

浏览器输入192.168.0.6:30066,显示如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_20

这样我在我的qq邮箱,1980570647@qq.com就可以收到报警了,如下

Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_句柄_21

配置Alertmanager报警-发送报警到钉钉

打开电脑版钉钉创建机器人

1.创建钉钉机器人

打开电脑版钉钉,创建一个群,创建自定义机器人,按如下步骤创建
https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq


我创建的机器人如下:
群设置-->智能群助手-->添加机器人-->自定义-->添加


机器人名称:kube-event
接收群组:钉钉报警测试


安全设置:
自定义关键词:cluster1


上面配置好之后点击完成即可,这样就会创建一个kube-event的报警机器人,创建机器人成功之后怎么查看webhook,按如下:


点击智能群助手,可以看到刚才创建的kube-event这个机器人,点击kube-event,就会进入到kube-event机器人的设置界面
出现如下内容:
机器人名称:kube-event
接受群组:钉钉报警测试
消息推送:开启
webhook:https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e
安全设置:
自定义关键词:cluster1

2.安装钉钉的webhook插件,在k8s的master1节点操作

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

prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz压缩包所在的百度网盘地址如下:

链接:https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ 
提取码:d59o

cd prometheus-webhook-dingtalk-0.3.0.linux-amd64

启动钉钉报警插件

nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e" &

对原来的文件做备份

cp alertmanager-cm.yaml alertmanager-cm.yaml.bak

重新生成一个新的alertmanager-cm.yaml文件

cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.163.com:25'
smtp_from: '15011572657@163.com'
smtp_auth_username: '15011572657'
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: cluster1
receivers:
- name: cluster1
webhook_configs:
- url: 'http://192.168.124.16:8060/dingtalk/cluster1/send'
send_resolved: true
EOF

通过kubectl apply使配置生效

kubectl delete -f alertmanager-cm.yaml

kubectl  apply  -f alertmanager-cm.yaml

kubectl delete -f prometheus-cfg.yaml

kubectl apply  -f prometheus-cfg.yaml

kubectl delete  -f prometheus-deploy.yaml

kubectl apply  -f  prometheus-deploy.yaml

通过上面步骤,就可以实现钉钉报警了​

接下来会给大家写通过alertmanager发送微信报警,根据报警级别实现钉钉+微信+邮箱同时报警,同时还会扩展prometheus监控,如监控tomcat、nginx、mysql、redis、mongodb等组件,也会介绍使用prometheus的pushgateway进行自定义数据的监控等,请关注我的公众号来持续学习。

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Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统-超详细文档_json_22