好記性不如爛筆頭，記下來便人便己：

1、搭建Prometheus的容器：

docker run -d --name prometheus -p 9090:9090 --network grafana -v $PWD/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus

其中:

①prometheus.yml檔案的預設配置可以從容器中掛載出來一份，也可以直接修改。我要訪問的的服務在本機。這裡有個坑，就是在容器裡面要訪問宿主機的ip的話，不能寫localhost。方法是：要麼將容器共享宿主機網路，這個不建議；要麼就直接寫宿主機的內網ip即可。

# 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: 'python-modules'

    static_configs:
    - targets: ['ip:80']

②建立一個grafana共享網路，使之與grafana容器處於同一網路空間

2、啟動grafana容器

docker run -d --name grafana --network grafana -p 3000:3000 grafana/grafana:6.6.2

3、 在grafana裡面配置好Prometheus的資料來源，即dashboard即可。完成後的樣例如下：