目录
prometheus-2.19.2.windows-amd64
1. 系统环境准备
windows_exporter-0.13.0-amd64
A Prometheus exporter for Windows machines. 可以帮助收集windows上的CPU、内存、磁盘、网络
Collectors 指标
| Name | Description | Enabled by default |
| ad | Active Directory Domain Services | |
| adfs | Active Directory Federation Services | |
| cpu | CPU usage | ✓ |
| cs | "Computer System" metrics (system properties, num cpus/total memory) | ✓ |
| container | Container metrics | |
| dhcp | DHCP Server | |
| dns | DNS Server | |
| exchange | Exchange metrics | |
| fsrmquota | Microsoft File Server Resource Manager (FSRM) Quotas collector | |
| hyperv | Hyper-V hosts | |
| iis | IIS sites and applications | |
| logical_disk | Logical disks, disk I/O | ✓ |
| logon | User logon sessions | |
| memory | Memory usage metrics | |
| msmq | MSMQ queues | |
| mssql | SQL Server Performance Objects metrics | |
| netframework_clrexceptions | .NET Framework CLR Exceptions | |
| netframework_clrinterop | .NET Framework Interop Metrics | |
| netframework_clrjit | .NET Framework JIT metrics | |
| netframework_clrloading | .NET Framework CLR Loading metrics | |
| netframework_clrlocksandthreads | .NET Framework locks and metrics threads | |
| netframework_clrmemory | .NET Framework Memory metrics | |
| netframework_clrremoting | .NET Framework Remoting metrics | |
| netframework_clrsecurity | .NET Framework Security Check metrics | |
| net | Network interface I/O | ✓ |
| os | OS metrics (memory, processes, users) | ✓ |
| process | Per-process metrics | |
| remote_fx | RemoteFX protocol (RDP) metrics | |
| service | Service state metrics | ✓ |
| system | System calls | ✓ |
| tcp | TCP connections | |
| thermalzone | Thermal information | |
| terminal_services | Terminal services (RDS) | |
| textfile | Read prometheus metrics from a text file | ✓ |
| vmware | Performance counters installed by the Vmware Guest agent |
有关报指标，配置设置和使用示例的更多信息，请参见每个收集器上的链接文档。
nvidia_smi_exporter
他是用于Prometheus的一个nvidia-smi metrics exporter ，可以帮助手机windows中的GPU信息，相当于执行了如下命令
nvidia-smi --query-gpu=name,index,temperature.gpu,utilization.gpu,utilization.memory,memory.total,memory.free,memory.used --format=csv,noheader,nounits
nvidia-smi.exe查看GPU信息
（1）打开cmd窗口（win+R，再输入cmd）
（2）进入C:\Program Files\NVIDIA Corporation\NVSMI中，执行nvidia-smi.exe
C:\Users\Administrator λ cd C:\Program Files\NVIDIA Corporation\NVSMI C:\Program Files\NVIDIA Corporation\NVSMI λ nvidia-smi.exe -l Fri Aug 28 10:58:21 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 446.14 Driver Version: 446.14 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce GTX 106... WDDM | 00000000:01:00.0 On | N/A | | 27% 36C P8 10W / 120W | 1079MiB / 6144MiB | 2% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU PID Type Process name GPU Memory | | Usage | |=============================================================================| | 0 680 C+G ...lPanel\SystemSettings.exe N/A | | 0 1388 C+G ...U-2019.1.3\bin\idea64.exe N/A | | 0 1704 C+G ...w5n1h2txyewy\SearchUI.exe N/A | | 0 2328 C+G ...gram\WeChat\WeChatApp.exe N/A | | 0 3220 C+G ...sk\baidunetdiskrender.exe N/A | | 0 5296 C+G ...y\ShellExperienceHost.exe N/A | | 0 5940 C+G ...me\Application\chrome.exe N/A | | 0 6976 C+G ...b3d8bbwe\WinStore.App.exe N/A | | 0 8636 C+G ...ekyb3d8bbwe\YourPhone.exe N/A | | 0 8664 C+G ...es.TextInput.InputApp.exe N/A | | 0 9880 C+G ...kyb3d8bbwe\Calculator.exe N/A | | 0 11552 C+G ...2701\Applet\WeChatApp.exe N/A | | 0 11560 C+G ...1055.127\DesktopMgr64.exe N/A | | 0 11864 C+G ...gram\WeChat\wechatweb.exe N/A | | 0 12840 C+G Insufficient Permissions N/A | | 0 13792 C+G C:\Windows\explorer.exe N/A | | 0 16004 C+G ...typora\program\Typora.exe N/A | +-----------------------------------------------------------------------------+
nvidia-smi 输出信息解释
表格中会显示显卡的一些信息，第一行是版本信息，第二行是标题栏，第三行就是具体的显卡信息了，如果有多个显卡，会有多行，每一行的信息值对应标题栏对应位置的信息。
GPU：编号，0
Name：显卡名，NVS 4200M
TCC/WDDM：WDDM
Fan：风扇转速，在0到100%之间变动。N/A，表示没转
Temp：显卡温度，这里是66摄氏度
Perf：性能状态，congP0-P12，P0性能最大，P12最小，这里是P0
Pwr：能耗，Usage是使用量，Cap是总量
BusId：涉及GPU总线
Disp.A：表示GPU的显示是否已经初始化
Memory-Usage：显存使用率，这里才用很少，5%不到
CPU-Util：GPU利用率
Compute M：计算模式
需要注意的一点是显存占用率和GPU占用率是两个不一样的东西，类似于内存和CPU，两个指标的占用率不一定是互相对应的。
prometheus-2.19.2.windows-amd64
下载地址：https://prometheus.io/download/
选择windows 版本即可
grafana-7.1.5.windows-amd64
下载地址：https://grafana.com/grafana/download
选择windows 版本即可
2. 安装wmi-exporter
- 下载地址：https://github.com/martinlindhe/wmi_exporter/releases选择最新msi文件下载安装即可。
- 完成安装后会自动创建一个开机自启的服务，通过服务(services.msc)判断是否有有windows_exporter
- 通过访问http://ip+默认端口9182（如：http://localhost:9182） 出现下图，即代表安装采集数据成功。
3. 安装nvidia_smi_exporter
依赖于go的编译环境
构建
> go build -v nvidia_smi_exporter
会自动生成一个nvidia_smi_exporter.exe文件
运行
> ./nvidia_smi_exporter [<port>]
Default port is 9101
访问：localhost:9101/metrics
temperature_gpu{gpu="TITAN X (Pascal)[0]"} 41 utilization_gpu{gpu="TITAN X (Pascal)[0]"} 0 utilization_memory{gpu="TITAN X (Pascal)[0]"} 0 memory_total{gpu="TITAN X (Pascal)[0]"} 12189 memory_free{gpu="TITAN X (Pascal)[0]"} 12189 memory_used{gpu="TITAN X (Pascal)[0]"} 0 temperature_gpu{gpu="TITAN X (Pascal)[1]"} 78 utilization_gpu{gpu="TITAN X (Pascal)[1]"} 95 utilization_memory{gpu="TITAN X (Pascal)[1]"} 59 memory_total{gpu="TITAN X (Pascal)[1]"} 12189 memory_free{gpu="TITAN X (Pascal)[1]"} 1738 memory_used{gpu="TITAN X (Pascal)[1]"} 10451 temperature_gpu{gpu="TITAN X (Pascal)[2]"} 83 utilization_gpu{gpu="TITAN X (Pascal)[2]"} 99 utilization_memory{gpu="TITAN X (Pascal)[2]"} 82 memory_total{gpu="TITAN X (Pascal)[2]"} 12189 memory_free{gpu="TITAN X (Pascal)[2]"} 190 memory_used{gpu="TITAN X (Pascal)[2]"} 11999 temperature_gpu{gpu="TITAN X (Pascal)[3]"} 84 utilization_gpu{gpu="TITAN X (Pascal)[3]"} 97 utilization_memory{gpu="TITAN X (Pascal)[3]"} 76 memory_total{gpu="TITAN X (Pascal)[3]"} 12189 memory_free{gpu="TITAN X (Pascal)[3]"} 536 memory_used{gpu="TITAN X (Pascal)[3]"} 11653
等价Command
nvidia-smi --query-gpu=name,index,temperature.gpu,utilization.gpu,utilization.memory,memory.total,memory.free,memory.used --format=csv,noheader,nounits
Prometheus 配置
- job_name: "gpu_exporter" static_configs: - targets: ['localhost:9101']
4. 安装prometheus
- 下载地址：https://prometheus.io/download/
- 选择windows 版本即可
prometheus配置
下载解压后，修改配置文件，修改新增windows_exporter、nvidia_smi_exporter数据采集器配置：
# my global config global: scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute. evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute. # scrape_timeout is set to the global default (10s). # Alertmanager configuration alerting: alertmanagers: - static_configs: - targets: # - alertmanager:9093 # Load rules once and periodically evaluate them according to the global 'evaluation_interval'. rule_files: # - "first_rules.yml" # - "second_rules.yml" # A scrape configuration containing exactly one endpoint to scrape: # Here it's Prometheus itself. scrape_configs: # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: 'Win-162' # metrics_path defaults to '/metrics' # scheme defaults to 'http'. static_configs: - targets: ['10.0.50.162:9182'] # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: 'Win-162-GPU' # metrics_path defaults to '/metrics' # scheme defaults to 'http'. static_configs: - targets: ['10.0.50.162:9101']
prometheus启动
启动，cmd进入安装目录（& – -> 后台运行）
prometheus.exe --config.file=prometheus.yml --web.listen-address=:9090 &
访问prometheus
访问 http://ip:9090 ,（如：http://localhost:9090）出现如下界面即为prometheus安装成功、数据采集器验证成功 （PS: up为成功，down 为不成功。）
5. 安装Grafana
- 下载地址：https://grafana.com/grafana/download
- 选择windows 版本即可
启动Grafana
解压完成，进入bin目录启动grafana-server.exe 打开浏览器访问 http://ip:3000,（如：http://localhost:3000） 用户名/密码 admin/admin
Grafana配置数据源
Configuration -- Add data source --- Prometheus ， 输入名称，prometheus的访问url ，点击保存测试，验证是否成功
Grafana选择监控仪表盘
访问地址：https://grafana.com/dashboards --- serch框输入windows ---- 选择心仪模板 (注意模板所支持的数据源为prometheus) ----- 进行 ID 或者JOSN文件两种方式导入。
推荐使用ID
仪表盘模板推荐
模板ID: 10467
-- Windows的Prometheus监控看板展示，增加了资源汇总展示，优化了明细展示。更新支持windows_exporter 0.13.0。
模板导入成功即可看到监控数据
但是他没有GPU信息，我们通过添加panel来显示GPU的温度和使用率，改造后的仪表盘JOSN文件
{ "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "description": "Windows的Prometheus监控看板展示，增加了资源汇总展示，优化了明细展示。更新支持windows_exporter 0.13.0。同时支持nvidia_smi_exporter的GPU温度和使用率展示", "editable": true, "gnetId": 10467, "graphTooltip": 1, "id": 1, "iteration": 1598863585509, "links": [ { "asDropdown": true, "icon": "external link", "tags": [], "targetBlank": true, "type": "dashboards" } ], "panels": [ { "collapsed": false, "datasource": "Prometheus", "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 41, "panels": [], "title": "资源总览（关联JOB项）当前选中主机：【$show_hostname】实例：$instance", "type": "row" }, { "columns": [], "datasource": "Prometheus", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fontSize": "80%", "gridPos": { "h": 6, "w": 24, "x": 0, "y": 1 }, "id": 45, "pageSize": null, "showHeader": true, "sort": { "col": 0, "desc": true }, "styles": [ { "alias": "操作系统", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "product", "preserveFormat": false, "sanitize": false, "thresholds": [], "type": "string", "unit": "short", "valueMaps": [] }, { "alias": "主机名", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "linkTooltip": "", "linkUrl": "", "mappingType": 1, "pattern": "hostname", "preserveFormat": false, "thresholds": [], "type": "string", "unit": "short" }, { "alias": "instance(连接到明细)", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": true, "linkTargetBlank": false, "linkTooltip": "${__cell_4}", "linkUrl": "/d/Kdh0OoSGz/?var-job=${__cell_5}&var-hostname=All&var-instance=${__cell_4}", "mappingType": 1, "pattern": "instance", "thresholds": [], "type": "string", "unit": "short" }, { "alias": "CPU核数", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #B", "thresholds": [], "type": "string", "unit": "short" }, { "alias": "总内存", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": null, "mappingType": 1, "pattern": "Value #C", "thresholds": [], "type": "number", "unit": "bytes" }, { "alias": "运行时间", "align": "auto", "colorMode": "cell", "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": null, "mappingType": 1, "pattern": "Value #D", "thresholds": [ "259200", "432000" ], "type": "number", "unit": "s" }, { "alias": "C盘使用率", "align": "auto", "colorMode": "cell", "colors": [ "rgba(50, 172, 45, 0.97)", "rgba(237, 129, 40, 0.89)", "rgba(245, 54, 54, 0.9)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #E", "thresholds": [ "60", "80" ], "type": "number", "unit": "percentunit" }, { "alias": "使用最多分区%", "align": "auto", "colorMode": "cell", "colors": [ "rgba(50, 172, 45, 0.97)", "rgba(237, 129, 40, 0.89)", "rgba(245, 54, 54, 0.9)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #F", "thresholds": [ "60", "80" ], "type": "number", "unit": "percentunit" }, { "alias": "进程数", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "link": false, "mappingType": 1, "pattern": "Value #G", "thresholds": [], "type": "string", "unit": "short" }, { "alias": "运行服务", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #H", "thresholds": [], "type": "string", "unit": "short" }, { "alias": "CPU使用率", "align": "auto", "colorMode": "cell", "colors": [ "rgba(50, 172, 45, 0.97)", "rgba(237, 129, 40, 0.89)", "rgba(245, 54, 54, 0.9)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #I", "thresholds": [ "50", "70" ], "type": "number", "unit": "percent" }, { "alias": "内存使用率", "align": "auto", "colorMode": "cell", "colors": [ "rgba(50, 172, 45, 0.97)", "rgba(237, 129, 40, 0.89)", "rgba(245, 54, 54, 0.9)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #J", "thresholds": [ "60", "80" ], "type": "number", "unit": "percent" }, { "alias": "CPU频率", "align": "auto", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "dateFormat": "YYYY-MM-DD HH:mm:ss", "decimals": 2, "mappingType": 1, "pattern": "Value #K", "thresholds": [], "type": "number", "unit": "rothz" }, { "alias": "", "align": "right", "colorMode": null, "colors": [ "rgba(245, 54, 54, 0.9)", "rgba(237, 129, 40, 0.89)", "rgba(50, 172, 45, 0.97)" ], "decimals": 2, "pattern": "/.*/", "thresholds": [], "type": "hidden", "unit": "short" } ], "targets": [ { "expr": "windows_os_info{job=~\"$job\"} * on(instance) group_right(product) windows_cs_hostname", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "A" }, { "expr": "time() - windows_system_system_up_time{job=~\"$job\"}", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "D" }, { "expr": "windows_cs_logical_processors{job=~\"$job\"} - 0", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "B" }, { "expr": "avg by (instance) (windows_cpu_core_frequency_mhz{job=~\"$job\"})", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "K" }, { "expr": "100 - (avg by (instance) (irate(windows_cpu_time_total{job=~\"$job\",mode=\"idle\"}[2m])) * 100)", "format": "table", "instant": true, "interval": "", "legendFormat": "CPU使用率", "refId": "I" }, { "expr": "windows_cs_physical_memory_bytes{job=~\"$job\"} - 0", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "C" }, { "expr": "100 - 100 * windows_os_physical_memory_free_bytes{job=~\"$job\"} / windows_cs_physical_memory_bytes{job=~\"$job\"}", "format": "table", "instant": true, "interval": "", "legendFormat": "内存使用率", "refId": "J" }, { "expr": "1 - (windows_logical_disk_free_bytes{job=~\"$job\",volume=~\"C:\"}/windows_logical_disk_size_bytes{job=~\"$job\",volume=~\"C:\"})", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "E" }, { "expr": "max by (instance) (1-windows_logical_disk_free_bytes/windows_logical_disk_size_bytes)", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "F" }, { "expr": "windows_os_processes{job=~\"$job\"}", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "G" }, { "expr": "sum by (instance) (windows_service_state{job=~\"$job\",state=~\"running\"})", "format": "table", "instant": true, "interval": "", "legendFormat": "", "refId": "H" } ], "timeFrom": null, "timeShift": null, "title": "$job：服务器资源总览", "transform": "table", "type": "table-old" }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "Prometheus", "decimals": 1, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 6, "w": 8, "x": 0, "y": 7 }, "hiddenSeries": false, "id": 47, "legend": { "alignAsTable": true, "avg": true, "current": true, "max": true, "min": false, "rightSide": true, "show": false, "sort": "current", "sortDesc": false, "total": false, "values": true }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "paceLength": 10, "percentage": false, "pluginVersion": "7.1.5", "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "100 - (avg by (instance) (irate(windows_cpu_time_total{job=~\"$job\",mode=\"idle\"}[2m])) * 100)", "hide": false, "interval": "", "intervalFactor": 1, "legendFormat": "{{instance}}", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "各主机CPU使用率", "tooltip": { "shared": true, "sort": 2, "value_type": "cumulative" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "percent", "label": null, "logBase": 1, "max": null, 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"refresh": false, "schemaVersion": 26, "style": "dark", "tags": [ "Prometheus", "windows_exporter" ], "templating": { "list": [ { "allValue": null, "current": { "selected": false, "text": "Win-225", "value": "Win-225" }, "datasource": "Prometheus", "definition": "label_values(windows_cs_hostname, job)", "hide": 0, "includeAll": false, "label": "JOB", "multi": false, "name": "job", "options": [], "query": "label_values(windows_cs_hostname, job)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 1, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "selected": false, "text": "All", "value": "$__all" }, "datasource": "Prometheus", "definition": "label_values(windows_cs_hostname{job=~\"$job\"}, hostname)", "hide": 0, "includeAll": true, "label": "主机名", "multi": false, "name": "hostname", "options": [], "query": "label_values(windows_cs_hostname{job=~\"$job\"}, hostname)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "selected": false, "text": "10.0.50.225:9182", "value": "10.0.50.225:9182" }, "datasource": "Prometheus", "definition": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "hide": 0, "includeAll": false, "label": "instance", "multi": false, "name": "instance", "options": [], "query": "label_values(windows_cs_hostname{job=~\"$job\",hostname=~\"$hostname\"}, instance)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 5, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "selected": false, "text": "SKY-20190610LFO", "value": "SKY-20190610LFO" }, "datasource": "Prometheus", "definition": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "hide": 2, "includeAll": false, "label": "展示使用的主机名", "multi": false, "name": "show_hostname", "options": [], "query": "label_values(windows_cs_hostname{job=~\"$job\",instance=~\"$instance\"}, hostname)", "refresh": 1, "regex": "", "skipUrlSync": false, "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-3h", "to": "now" }, "timepicker": { "refresh_intervals": [ "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "Windows Overriew", "uid": "Kdh0OoSGz", "version": 2 }
6. 自动化启动脚本
@echo off echo. echo Windows指标采集[开始准备] echo. echo CPU\Mem\Disk\Net采集[开始] echo 依赖于windows_exporter-0.13.0-amd64服务，完成安装后会自动创建一个开机自启的服务，通过服务(services.msc)判断有windows_exporter echo CPU\Mem\Disk\Net采集[开始]-- 可以通过 http://localhost:9182/metrics 访问 echo GPU采集[开始] start /d "D:\JT\java\prometheus\nvidia_smi_exporter" nvidia_smi_exporter.exe & ping localhost -n 2 ::urlstatus=$(curl -X GET -i http://localhost:9101/metrics | grep 200) ::if [ "$urlstatus" != "" ];then :: echo "GPU采集准备好了" ::fi echo "GPU采集[完成]-- 可以通过 http://localhost:9101/metrics 访问" echo Prometheus[开始] start /d "D:\JT\java\prometheus\prometheus-2.19.2.windows-amd64" prometheus.exe --config.file=prometheus.yml --web.listen-address=:9090 & ping localhost -n 30 echo Prometheus[完成] -- 可以通过 http://localhost:9090 访问 echo Grafana[开始] start /d "D:\JT\java\prometheus\grafana-7.1.5.windows-amd64\grafana-7.1.5\bin" grafana-server.exe & ping localhost -n 30 echo Grafana[完成] -- 可以通过 http://localhost:3000/d/Kdh0OoSGz/win-162?orgId=1 访问 【账号：admin 密码：123456】 ::执行完命令后关闭cmd命令窗口 ::设置开机时运行 ::bat脚本复制到此文件夹C:\ProgramData\Microsoft\Windows\Start Menu\Programs\StartUp echo Windows指标采集[准备完毕] ::taskkill /f /im cmd.exe ::exit pause
