好记性不如烂笔头，记下来便人便己：
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文件的默认配置可以从容器中挂载出来一份，也可以直接修改。我要访问的的服务在本机。这里有个坑，就是在容器里面要访问宿主机的ip的话，不能写localhost。方法是：要么将容器共享宿主机网络，这个不建议；要么就直接写宿主机的内网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创建一个grafana共享网络，使之与grafana容器处于同一网络空间
2、启动grafana容器
docker run -d --name grafana --network grafana -p 3000:3000 grafana/grafana:6.6.2
3、 在grafana里面配置好Prometheus的数据源，即dashboard即可。完成后的样例如下：
