promethues

前言：

最近项目中用到了promethues做服务告警，这里就把自己探索过程中收集到的文档整理出来，以及一些自己使用过程中的东西

简介

Prometheus 是一套开源的系统监控报警框架。它启发于 Google 的 borgmon 监控系统，由工作在 SoundCloud 的
google 前员工在 2012 年创建，作为社区开源项目进行开发，并于 2015 年正式发布。2016 年，Prometheus 正式加入
Cloud Native Computing Foundation，成为受欢迎度仅次于 Kubernetes 的项目。

有人可能会疑惑：promethues和grafana结合岂不是可以把预警、日志分析全做了？
其实官方强烈不推荐使用promethues和grafana做日志分析的，官方解释日志分析还是交由专业的ELK去做，不建议使用promethues和grafana。

作为新一代的监控框架，Prometheus 具有以下特点：

强大的多维度数据模型：
时间序列数据通过 metric 名和键值对来区分。
所有的 metrics 都可以设置任意的多维标签。
数据模型更随意，不需要刻意设置为以点分隔的字符串。
可以对数据模型进行聚合，切割和切片操作。
支持双精度浮点类型，标签可以设为全 unicode。
灵活而强大的查询语句（PromQL）：在同一个查询语句，可以对多个 metrics 进行乘法、加法、连接、取分数位等操作。
易于管理： Prometheus server 是一个单独的二进制文件，可直接在本地工作，不依赖于分布式存储。
高效：平均每个采样点仅占 3.5 bytes，且一个 Prometheus server 可以处理数百万的 metrics。
使用 pull 模式采集时间序列数据，这样不仅有利于本机测试而且可以避免有问题的服务器推送坏的 metrics。
可以采用 push gateway 的方式把时间序列数据推送至 Prometheus server 端。
可以通过服务发现或者静态配置去获取监控的 targets。
有多种可视化图形界面。
易于伸缩。

需要指出的是，由于数据采集可能会有丢失，所以 Prometheus 不适用对采集数据要 100% 准确的情形。但如果用于记录时间序列数据，Prometheus 具有很大的查询优势，此外，Prometheus 适用于微服务的体系架构

组成&工作流

架构

如图所示，即为promethues的架构图

概念介绍

Prometheus Server：

Prometheus Sever是Prometheus组件中的核心部分，负责实现对监控数据的获取，存储及查询。Prometheus
Server可以通过静态配置管理监控目标，也可以配合使用Service
Discovery的方式动态管理监控目标，并从这些监控目标中获取数据。其次Prometheus
Sever需要对采集到的数据进行存储，Prometheus
Server本身就是一个实时数据库，将采集到的监控数据按照时间序列的方式存储在本地磁盘当中。Prometheus
Server对外提供了自定义的PromQL，实现对数据的查询以及分析。另外Prometheus
Server的联邦集群能力可以使其从其他的Prometheus Server实例中获取数据。

Exporters：

Exporter将监控数据采集的端点通过HTTP服务的形式暴露给Prometheus Server，Prometheus
Server通过访问该Exporter提供的Endpoint端点，即可以获取到需要采集的监控数据。可以将Exporter分为2类：

直接采集：

这一类Exporter直接内置了对Prometheus监控的支持，比如cAdvisor，Kubernetes，Etcd，Gokit等，都直接内置了用于向Prometheus暴露监控数据的端点。

间接采集：

原有监控目标并不直接支持Prometheus，因此需要通过Prometheus提供的Client
Library编写该监控目标的监控采集程序。例如：Mysql Exporter，JMX Exporter，Consul Exporter等。

AlertManager：

在Prometheus Server中支持基于Prom QL创建告警规则，如果满足Prom
QL定义的规则，则会产生一条告警。在AlertManager从 Prometheus server 端接收到
alerts后，会进行去除重复数据，分组，并路由到对收的接受方式，发出报警。常见的接收方式有：电子邮件，pagerduty，webhook
等。

PushGateway:

Prometheus数据采集基于Prometheus Server从Exporter pull数据，因此当网络环境不允许Prometheus
Server和Exporter进行通信时，可以使用PushGateway来进行中转。通过PushGateway将内部网络的监控数据主动Push到Gateway中，Prometheus
Server采用针对Exporter同样的方式，将监控数据从PushGateway pull到Prometheus Server。

Prometheus的工作流：

pull(拉取)

Prometheus
server定期从配置好的jobs或者exporters中拉取metrics(所有拉取规则都是在rules.yml配置文件中配置)，或者接收来自Pushgateway发送过来的metrics，或者从其它的Prometheus
server中拉metrics。

存储

Prometheus server在本地存储收集到的metrics，并运行定义好的alerts.rules，记录新的时间序列或者向Alert
manager推送警报。

预警

Alertmanager根据配置文件，对接收到的警报进行处理，发出告警。

展示

在图形界面中，可视化采集数据。可以采用Grafana做最后的图形展示。

安装和配置

文中promethues相关下载链接(提前公示),点击此处即可跳转

包含以下:

prometheus

alertmanager

node_exporter

其他的官方提供的exporter(提前公示),点击此处即可跳转

一、oracle监控安装

它是用来监控oracle数据库的，可以和oracle安装在同一个服务器，也可以不同，这里建议还是不要装在一台服务器上，对数据库服务器是有性能上的影响的。

1.1 首先需要下载以下安装包：

oracledb_exporter.0.2.2.linux-amd64.tar.gz    ===>地址：https://prometheus.io/docs/instrumenting/exporters/

以下三个地址：====>https://www.oracle.com/database/technologies/instant-client/linux-x86-64-downloads.html
oracle-instantclient18.5-basic-18.5.0.0.0-3.x86_64.rpm
oracle-instantclient18.5-devel-18.5.0.0.0-3.x86_64.rpm
oracle-instantclient18.5-sqlplus-18.5.0.0.0-3.x86_64.rpm

1.2 安装：

1.2.1 如果是centos系统可以直接安装这三个oracle客户端组件，安装命令：

rpm -ivh 包全名

1.2.2 如果是Ubuntu系统，则需要转换为Ubuntu的安装包，再进行安装，转换命令如下：

sudo apt-get install alien               #alien默认没有安装，所以首先要安装它

sudo alien xxxx.rpm #将rpm转换位deb，完成后会生成一个同名的xxxx.deb

sudo dpkg -i xxxx.deb #安装

1.3 环境变量设置：

修改环境变量

vim /etc/profile

文件末尾追加这三个环境变量

export DATA_SOURCE_NAME=oracle_exporter/Wl4L7CDM5@//172.30.130.116:1521/orcl   #这里填写监控用的用户名及密码，需要注意的是，这个用户必须具有dba权限。
export LD_LIBRARY_PATH=/usr/lib/oracle/18.5/client64/lib:/usr/lib/oracle/18.5/client64   #这里是oracle客户端安装完后的位置，自行去'/usr/lib/oracle/'这个目录下核查目录路径是否正确，如果版本号不对，手动修改这个值
export ORACLE_HOME=/usr/lib/oracle/18.5/client64   #核对目录并检查版本号

之后使环境变量生效即可

source /etc/profile

1.4 安装oracledb_exporter

他不需要安装，解压即可使用

tar -zxvf oracledb_exporter.0.2.2.linux-amd64.tar.gz 
cd oracledb_exporter.0.2.2.linux-amd64
nohup ./oracledb_exporter &

二、 node_exporter安装

它是用来监控各节点的状态，需要安装在各需要监控的服务器上，一个服务器安装一个。下载的地址如下：

https://prometheus.io/docs/instrumenting/exporters/

在上述地址请求后的页面中找到要下载的node_exporter，并下载。
下载后：

1. mkdir  -p  /server/deployment/
2. 将node_exporter-0.18.1.linux-amd64.tar.gz移至/server/deployment/
3. 解压node_exporter-0.18.1.linux-amd64.tar.gz =====》命令： tar -zxvf node_exporter-0.18.1.linux-amd64.tar.gz
4. 进入到/server/deployment/node_exporter-0.18.1.linux-amd64 ===>命令： cd /server/deployment/node_exporter-0.18.1.linux-amd64 
5. nohup  /server/deployment/node_exporter-0.18.1.linux-amd64/node_exporter &       #后台启动即可

他的默认监听端口是9100，如果发现与本机的服务端口有所冲突，可以指定其监听的端口。但是需要注意的是，修改完监听端口后，一定要在 3.2.1 prometheus.yml配置 中修改你所指定的这个端口。
修改默认端口的命令(将下面的9100改为你所要监听的指定端口)：

nohup  /server/deployment/node_exporter-0.18.1.linux-amd64/node_exporter  --web.listen-address=":9100"  &

三、安装promethues

他是用来汇总监控信息的中央控制器，并且会发送告警信息给alertmanager

3.1 下载prometheus-2.15.2.linux-amd64.tar.gz

 https://prometheus.io/download/

选择自己需要的版本进行下载

3.2 解压即可用，重要的是需要进行配置

注意:

这里的格式一定不要错乱，要保证严格的yml格式，比如最简单的各种对齐，否则启动会报错

项目中有的机器会出现中文不兼容的问题，所以最好将我下文中的模板中中文注释去除

3.2.1 prometheus.yml配置

global:
  scrape_interval:     15s   #全局的间隔请求时间
  evaluation_interval: 15s  #指的是计算rule的间隔
  scrape_timeout: 10s    #间隔请求超时时间，这个是不能大于scrape_interval的
  external_labels:
   monitor: 'codelab_monitor'

alerting:
 alertmanagers:       #这个是alertmanager的配置，用来最后发出告警信息的模块，在哪安装的他，就配置安装的信息即可，他的默认端口号是9093
 - static_configs:
   - targets: ["localhost:9093"]

rule_files:
 - "/server/prometheus-2.18.0-rc.0.linux-amd64/rules.yml"    #prometheus的告警规则配置文件地址

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
    - targets: ['localhost:9090']

  - job_name: 'node'    #各节点的配置信息，这里配置的主要是node_exporter的监控信息，如果你没有对他的默认端口进行修改，那么就写9100即可
    static_configs:
    - targets: ['{IP}:9100']  #为了通用性，我将服务器ip换成了{IP}，使用时将其替换成真正的ip地址即可

  - job_name: 'nginx'
    scrape_interval: 5s   #在这里配置这个请求间隔时间是会覆盖掉global里的全局间隔时间的
    static_configs:
    - targets: ['{IP}:9913']

  - job_name: 'oracle'
    scrape_interval: 10s
    static_configs:
    - targets: ['localhost:9161']    #这些端口号并不是随意填写的，他们都是各个监控器的监听端口，这个是oracledb_exporter

  - job_name: 'redis'
    scrape_interval: 10s
    static_configs:
    - targets: ['{IP1}:9121','{IP2}:9121']

  - job_name: ‘rabbit_mq'
    scrape_interval:10s
    static_configs:
    - targets: ['{IP1}:9419']

3.2.2 rules.yml配置

在prometheus文件夹下新建rules.yml,这个是告警规则，有的服务器不支持中文，有的支持，不支持的话翻译成英文即可，当然，写英文的更好，防止乱码的出现。部署时，建议务必删除下文中我的中文注释，以防报错

groups:
- name: hostStatsAlert
  rules:
  - alert: "memory-warning"     #内存告警规则，持续1分钟大于90%即可告警
    expr: ((node_memory_MemTotal_bytes-(node_memory_MemFree_bytes+node_memory_Buffers_bytes+node_memory_Cached_bytes))/node_memory_MemTotal_bytes)*100>90
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "{{ $labels.instance }} Server memory usage is over 90%"
      description: "{{ $labels.instance }} Server memory usage is over 90%, the current value is: {{ $value }}%"
  - alert: "CPU-warning"      #CPU告警规则，使用率持续1分钟大于90%，即可告警
    expr: round(100-((avg by (instance,job)(irate(node_cpu_seconds_total{mode="idle",instance!~'bac-.*'}[5m]))) *100))>90
    for: 1m
    labels:
      severity: warning
    annotations:
      summary: "{{ $labels.instance }} Excessive CPU utilization"
      description: "{{ $labels.instance }} Excessive CPU utilization, current utilization is: {{ $value }}%"
  - alert: "diskSpace-warning"    #磁盘空间告警规则，可用磁盘小于5G持续10秒，即可告警
    expr: round(node_filesystem_avail_bytes{fstype=~"ext4|xfs",instance!~"testnode",mountpoint!~"/boot.*"}/1024/1024/1024) < 5
    for: 10s
    labels:
      severity: warning
    annotations:
      summary: "{{ $labels.instance }} Insufficient disk space"
      description: "{{ $labels.instance }} Insufficient disk space, current disk space is: {{$labels.mountpoint}} remaining: {{ $value }}GB"
  - alert: "nodeStatus-warning"  #节点存活状态告警规则，1就是正常，0就是挂了，如果持续15秒都是挂的状态，即可告警
    expr: up == 0
    for: 15s
    labels:
      severity: warning
    annotations:
      summary: "{{ $labels.instance }} Machine survival test"
      description: "{{ $labels.instance }} The machine is down, please check!"
  - alert: "oracle-warning"     #oracle存活状态告警规则，1就是正常，0就是挂了，如果持续15秒都是挂的状态，即可告警
    expr: probe_success == 0
    for: 15s
    labels:
      severity: warning
    annotations:
      summary: "{{ $labels.job }} oracle is breakdown"
      description: "{{ $labels.job }} oracle is breakdown"

3.3 启动

nohup /server/prometheus-2.15.2.linux-amd64/prometheus --config.file=/server/prometheus-2.15.2.linux-amd64/prometheus.yml &

说明： 如果需要新增其他的监控，比如rabbit mq、redis等，需要注意的有三点：

1) 到[监控插件下载地址](https://prometheus.io/docs/instrumenting/exporters/ )   这个地址去下载相关的exporter，大部分都是开箱即用，解压即可使用，当然也会有需要填写一些附带信息的。
2）接着需要修改prometheus.yml，新增一个job_name模块即可。
3）接着需要修改rules.yml，新增相关的告警指标即可。具体的指标可以访问prometheus提供的页面http://172.16.52.37:9090/targets，（就是prometheus的默认提供服务的接口：9090，在那边可以找到很多参数指标，可自行组合设置）
4）最后重启prometheus即可，可以直接kill掉原来的，直接重启即可。

四、安装Alertmanager

alertmanager是告警模块，他负责将prometheus的告警信息收集并统一发送出去，里面可以直接配置钉钉的机器人接口地址，具体方法可以网上搜寻一下，我们是使用web.hook将告警信息发送到指定的web服务里，将其进行一些处理后在发送给钉钉。

4.1 下载

https://prometheus.io/download/

在上述地址中可以找到alertmanager的下载。下载完后将其解压，配置基本信息即可配合prometheus使用

4.2 配置

编辑alertmanager.yml

global:
  resolve_timeout: 5m
route:
  group_by: ['prometheus']
  group_wait: 10s
  group_interval: 10s
  repeat_interval: 10m
  receiver: 'web.hook'
receivers:
  - name: 'web.hook'
    webhook_configs:
      - url: 'http://{IP}:8012/api/messages/receive'   #web服务接收告警信息地址
        http_config:
          bearer_token: LhTIaDGXAu1yAeKiAWobgZDo0GMQZR6l    #设置这个token，增加相应的安全性，这个token是在header里

inhibit_rules:
  - source_match:
      severity: 'critical'
    target_match:
      severity: 'warning'
    equal: ['alertname', 'dev', 'instance']

4.3 启动

nohup ./alertmanager --config.file=/server/alertmanager-0.20.0.linux-amd64/alertmanager.yml &

五、安装Grafana

5.1 下载

https://grafana.com/grafana/download

下载Grafana,解压后直接进入bin目录,执行下面的命令即可启动，grafana最麻烦的地方在于配置监控信息

./grafana-server web

5.2 配置

默认端口3000，{部署的服务器ip}:3000 即可访问页面。默认的初始用户名/密码：admin/admin
第一次登陆会提示修改密码。修改即可。
进入后，如下图所示操作即可

5.2.1 配置数据源（prometheus）

保存即可

5.2.2 配置监控面板

导入模板

这里有技巧，你可以自己写，但是会很麻烦，费力不讨好，自己写一些模板里没有的特定的还是可以的，其他的还是建议直接导入模板
这里有你想要的各类模板

https://grafana.com/grafana/dashboards?orderBy=name&direction=asc

这里有已经调试过得两个模板,新建两个json文件，将他们的内容保存进去，文件见七、附件

保存完了之后

点击这些监控面板即可，可以看到很多监控信息

六、总结

6.1 怎么知道自己有没有启动成功prometheus？

答：

http://{prometheus部署的ip}:9090

直接访问即可，如果部署成功，会有web页面的

6.2 需要注意的地方：

6.2.1 防火墙要开几个特殊的端口

比如这个机子上安装的各类监控器的监听端口：9100、9090等等，按需开启，centos的开启命令.
如果你没有开启，那么会导致你的监控器无法访问

firewall-cmd --zone=public --add-port=9090/tcp --permanent
systemctl restart firewalld.service

6.2.2 服务器时间

项目中有碰到过因服务器时间未同步导致的grafana无法获取信息的问题，所以以上完成后，请检查一下自己的服务器时间是否已经同步

七、附件

Oracledb监控-性能与表空间-1587890196110.json

{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": "Prometheus",
        "enable": false,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "limit": 100,
        "name": "Annotations & Alerts",
        "showIn": 0,
        "type": "dashboard"
      }
    ]
  },
  "description": "使用oracledb-exporter进行oracle数据库监控，相对其他dashboard增加了tablespace的监控，可拓展设置为表空间告警；\n\n",
  "editable": true,
  "gnetId": 11121,
  "graphTooltip": 1,
  "id": 17,
  "iteration": 1587890174657,
  "links": [
    {
      "asDropdown": true,
      "icon": "external link",
      "tags": [],
      "type": "dashboards"
    }
  ],
  "panels": [
    {
      "collapsed": false,
      "gridPos": {
        "h": 1,
        "w": 24,
        "x": 0,
        "y": 0
      },
      "id": 67,
      "panels": [],
      "title": "数据库",
      "type": "row"
    },
    {
      "cacheTimeout": null,
      "colorBackground": false,
      "colorValue": true,
      "colors": [
        "rgba(245, 54, 54, 0.9)",
        "rgba(237, 129, 40, 0.89)",
        "rgba(50, 172, 45, 0.97)"
      ],
      "datasource": "Prometheus",
      "decimals": null,
      "editable": true,
      "error": false,
      "format": "none",
      "gauge": {
        "maxValue": 100,
        "minValue": 0,
        "show": false,
        "thresholdLabels": false,
        "thresholdMarkers": true
      },
      "gridPos": {
        "h": 3,
        "w": 3,
        "x": 0,
        "y": 1
      },
      "height": "125px",
      "id": 12,
      "interval": "$interval",
      "links": [],
      "mappingType": 1,
      "mappingTypes": [
        {
          "name": "value to text",
          "value": 1
        },
        {
          "name": "range to text",
          "value": 2
        }
      ],
      "maxDataPoints": 100,
      "nullPointMode": "connected",
      "nullText": null,
      "options": {},
      "postfix": "",
      "postfixFontSize": "80%",
      "prefix": "",
      "prefixFontSize": "80%",
      "rangeMaps": [
        {
          "from": "null",
          "text": "N/A",
          "to": "null"
        }
      ],
      "sparkline": {
        "fillColor": "rgba(31, 118, 189, 0.18)",
        "full": false,
        "lineColor": "rgb(31, 120, 193)",
        "show": false
      },
      "tableColumn": "",
      "targets": [
        {
          "calculatedInterval": "10m",
          "datasourceErrors": {},
          "errors": {},
          "expr": "oracledb_up{instance=\"$host\"}",
          "format": "time_series",
          "interval": "5m",
          "intervalFactor": 1,
          "legendFormat": "",
          "metric": "",
          "refId": "A",
          "step": 300
        }
      ],
      "thresholds": "0,1",
      "title": "数据库状态",
      "type": "singlestat",
      "valueFontSize": "100%",
      "valueMaps": [
        {
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服务器监控面板-1587890262488.json

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          "refId": "D"
        }
      ],
      "thresholds": [],
      "timeFrom": null,
      "timeRegions": [],
      "timeShift": null,
      "title": "每次IO读写的耗时（参考：小于100ms）(beta)",
      "tooltip": {
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        "sort": 2,
        "value_type": "individual"
      },
      "type": "graph",
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        "show": true,
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      },
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        {
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          "label": "读取（-）/写入（+）",
          "logBase": 1,
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          "min": null,
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        },
        {
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        }
      ],
      "yaxis": {
        "align": false,
        "alignLevel": null
      }
    },
    {
      "aliasColors": {
        "192.168.200.241:9100_TCP_alloc": "semi-dark-blue",
        "TCP": "#6ED0E0"
      },
      "bars": false,
      "dashLength": 10,
      "dashes": false,
      "datasource": "Prometheus",
      "description": "Sockets_used - 已使用的所有协议套接字总量\n\nCurrEstab - 当前状态为 ESTABLISHED 或 CLOSE-WAIT 的 TCP 连接数\n\nTCP_alloc - 已分配（已建立、已申请到sk_buff）的TCP套接字数量\n\nTCP_tw - 等待关闭的TCP连接数\n\nUDP_inuse - 正在使用的 UDP 套接字数量",
      "fill": 1,
      "fillGradient": 0,
      "gridPos": {
        "h": 6,
        "w": 24,
        "x": 0,
        "y": 43
      },
      "height": "300",
      "id": 158,
      "interval": "",
      "legend": {
        "alignAsTable": true,
        "avg": true,
        "current": true,
        "hideEmpty": true,
        "hideZero": true,
        "max": true,
        "min": false,
        "rightSide": false,
        "show": true,
        "sort": "current",
        "sortDesc": true,
        "total": false,
        "values": true
      },
      "lines": true,
      "linewidth": 2,
      "links": [],
      "nullPointMode": "null",
      "options": {
        "dataLinks": []
      },
      "paceLength": 10,
      "percentage": false,
      "pointradius": 5,
      "points": false,
      "renderer": "flot",
      "seriesOverrides": [
        {
          "alias": "/.*_Sockets_used/",
          "color": "#C4162A",
          "fill": 0
        }
      ],
      "spaceLength": 10,
      "stack": false,
      "steppedLine": false,
      "targets": [
        {
          "expr": "node_netstat_Tcp_CurrEstab{instance=~'$node'}",
          "format": "time_series",
          "hide": false,
          "instant": false,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}_CurrEstab",
          "refId": "A",
          "step": 20
        },
        {
          "expr": "node_sockstat_TCP_tw{instance=~'$node'}",
          "format": "time_series",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}_TCP_tw",
          "refId": "D"
        },
        {
          "expr": "node_sockstat_sockets_used{instance=~'$node'}",
          "format": "time_series",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}_Sockets_used",
          "refId": "B"
        },
        {
          "expr": "node_sockstat_UDP_inuse{instance=~'$node'}",
          "format": "time_series",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}_UDP_inuse",
          "refId": "C"
        },
        {
          "expr": "node_sockstat_TCP_alloc{instance=~'$node'}",
          "format": "time_series",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}_TCP_alloc",
          "refId": "E"
        }
      ],
      "thresholds": [],
      "timeFrom": null,
      "timeRegions": [],
      "timeShift": null,
      "title": "网络连接信息",
      "tooltip": {
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        "sort": 2,
        "value_type": "individual"
      },
      "type": "graph",
      "xaxis": {
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      },
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          "show": true
        }
      ],
      "yaxis": {
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        "alignLevel": null
      }
    }
  ],
  "refresh": "",
  "schemaVersion": 18,
  "style": "dark",
  "tags": [
    "Prometheus",
    "node_exporter",
    "StarsL.cn"
  ],
  "templating": {
    "list": [
      {
        "allValue": null,
        "current": {
          "text": "node",
          "value": "node"
        },
        "datasource": "Prometheus",
        "definition": "label_values(node_uname_info, job)",
        "hide": 0,
        "includeAll": false,
        "label": "JOB",
        "multi": false,
        "name": "job",
        "options": [],
        "query": "label_values(node_uname_info, job)",
        "refresh": 1,
        "regex": "",
        "skipUrlSync": false,
        "sort": 1,
        "tagValuesQuery": "",
        "tags": [],
        "tagsQuery": "",
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      },
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        },
        "datasource": "Prometheus",
        "definition": "label_values(node_uname_info{job=~\"$job\"}, nodename)",
        "hide": 0,
        "includeAll": true,
        "label": "主机名",
        "multi": true,
        "name": "hostname",
        "options": [],
        "query": "label_values(node_uname_info{job=~\"$job\"}, nodename)",
        "refresh": 1,
        "regex": "",
        "skipUrlSync": false,
        "sort": 0,
        "tagValuesQuery": "",
        "tags": [],
        "tagsQuery": "",
        "type": "query",
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      },
      {
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        "current": {
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        },
        "datasource": "Prometheus",
        "definition": "label_values(node_uname_info{nodename=~\"$hostname\"},instance)",
        "hide": 0,
        "includeAll": true,
        "label": "IP( 自动关联主机名)",
        "multi": false,
        "multiFormat": "regex values",
        "name": "node",
        "options": [],
        "query": "label_values(node_uname_info{nodename=~\"$hostname\"},instance)",
        "refresh": 2,
        "regex": "",
        "skipUrlSync": false,
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        "tagValuesQuery": "",
        "tags": [],
        "tagsQuery": "",
        "type": "query",
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      },
      {
        "allValue": null,
        "current": {
          "text": "/server2t",
          "value": "/server2t"
        },
        "datasource": "Prometheus",
        "definition": "",
        "hide": 2,
        "includeAll": false,
        "label": "",
        "multi": false,
        "name": "maxmount",
        "options": [],
        "query": "query_result(topk(1,sort_desc (max(node_filesystem_size_bytes{instance=~'$node',fstype=~\"ext4|xfs\"}) by (mountpoint))))",
        "refresh": 2,
        "regex": "/.*\\\"(.*)\\\".*/",
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      },
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          "text": "None",
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        },
        "datasource": "Prometheus",
        "definition": "",
        "hide": 2,
        "includeAll": false,
        "label": "环境",
        "multi": false,
        "multiFormat": "regex values",
        "name": "env",
        "options": [],
        "query": "label_values(node_exporter_build_info,env)",
        "refresh": 2,
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        "current": {
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        },
        "datasource": "Prometheus",
        "definition": "label_values(node_exporter_build_info{env=~'$env'},name)",
        "hide": 2,
        "includeAll": false,
        "label": "名称",
        "multi": true,
        "multiFormat": "regex values",
        "name": "name",
        "options": [],
        "query": "label_values(node_exporter_build_info{env=~'$env'},name)",
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        "skipUrlSync": false,
        "sort": 1,
        "tagValuesQuery": "/.*/",
        "tags": [],
        "tagsQuery": "",
        "type": "query",
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      }
    ]
  },
  "time": {
    "from": "now-2d",
    "to": "now"
  },
  "timepicker": {
    "now": true,
    "refresh_intervals": [
      "5s",
      "10s",
      "30s",
      "1m",
      "5m",
      "15m",
      "30m",
      "1h",
      "2h",
      "1d"
    ],
    "time_options": [
      "5m",
      "15m",
      "1h",
      "6h",
      "12h",
      "24h",
      "2d",
      "7d",
      "30d"
    ]
  },
  "timezone": "browser",
  "title": "服务器监控面板",
  "uid": "9CWBz0bik1",
  "version": 7
}

参考文献

https://www.ctolib.com/docs/sfile/prometheus-book/quickstart/why-monitor.html
https://www.ibm.com/developerworks/cn/cloud/library/cl-lo-prometheus-getting-started-and-practice/index.html
https://blog.51cto.com/youerning/2050543?from=timeline
https://blog.csdn.net/qq_42606051/article/details/82625163
https://prometheus.io/docs/introduction/faq/

promethues监控体系