使用python测试prometheus

为了更直观的了解prometheus如何工作，本文使用prometheus的python库来做一些相应的测试。

python库的github地址是

​​https://github.com/prometheus/client_python​​

根据提示，使用pip安装prometheus_client

pip3 install prometheus_client

然后根据文档中的示例文件运行一个client

文件命名为prometheus_python_client.py

from prometheus_client import start_http_server, Summary
import random
import time

# Create a metric to track time spent and requests made.
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')

# Decorate function with metric.
@REQUEST_TIME.time()
def process_request(t):
    """A dummy function that takes some time."""
    time.sleep(t)

if __name__ == '__main__':
    # Start up the server to expose the metrics.
    start_http_server(8080)
    # Generate some requests.
    while True:
        process_request(random.random())

在后台运行client

pytho3 prometheus_python_client.py &

此时可以访问本机的8080端口，可以看到相应的metric

curl 127.0.0.1:8080/metrics

得到如图所示结果

为了能监控到这个端口为8080的目标，需要在prometheus的配置文件prometheus.yml进行一些修改

在scrape_configs块部分加上一个新的job

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-client'
    scrape_interval: 5s
    static_configs:
      - targets: ['localhost:8080']
        labels:
          group: 'python-client-group'

重启prometheus，并访问其web页面，在Expression中输入一个python client的metric并执行

可以看到对应的结果正如在scrape_configs中所配置的相一致。

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