Grafana 系列監控系列 - node_exporter 採集主機資訊

監控機ip 192.168.0.34

分為以下幾個步驟

1. 下載及運行Node_exporter 在監控機上
2. 配置本地prometheus 撈取監控機數據
3. 透過 Grafana 觀察撈取的數據

1. 下載及運行Node_exporter 在監控機上

Node_exporter github

針對主機 cpu 下載相對應的運行時，下載後會如下圖，運行在該機器的9100 port上面，之後在設定 prometheus 撈取數據存在。

# 該範例運行在intel 晶片上面  
wget https://github.com/prometheus/node_exporter/releases/download/v1.8.1/node_exporter-1.8.1.linux-amd64.tar.gz 
tar zxvf node_exporter-1.8.1.linux-amd64.tar.gz 
cd node_exporter-1.8.1.linux-amd64 
nohup ./node_exporter &

2. 配置本地prometheus 撈取監控機數據

新增配置，並且重新啟動，可以到prometheus 查看配置是否成功，在Status->Targets。

# 配置在 /opt/bitnami/prometheus/conf/prometheus.yml 
# 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: "prometheus" 
 
    # metrics_path defaults to '/metrics' 
    # scheme defaults to 'http'. 
 
    static_configs: 
      - targets: ["localhost:9090"] 
  - job_name: "local" # <-- 新增配置 選定機器ip 
    static_configs: 
      - targets: ["192.168.0.34:9100"] 
        labels: # <-- 若想要分組 可以透過 標籤 依照面板設定 
          name: myname-2 
          job: test-2

透過 Grafana 觀察撈取的數據

Grafana 面板 如果要中文可以選擇，若想要分群組 可以新增參數

中文 8919

英文 1860