用python编写exporter监控接口访问数量+错误率+响应时间

1.编写exporter

import prometheus_clientfrom prometheus_client import Gauge,start_http_server,Counterimport pycurlimport timeimport threadingfrom io import BytesIO#创建client_python中提供的prometheus urlunter数据类型http_code = Counter("url_http_code", "request http_code of the host",['code','url'])url_http_request_time = Counter("url_http_request_time", "request http_request_time of the host",['le','url'])http_request_total = Counter("http_request_total", "request request total of the host",['url'])#curl url，返回状态码和总共需要时间 -- 返回状态和响应时间deff test_website(url):    buffer_curl = BytesIO()    c = pycurl.Curl()    c.setopt(pycurl.URL, url)#    c.setopt(pycurl.WRITEDATA, buffer_curl)    c.setopt(pycurl.CONNECTTIMEOUT, 3)    c.setopt(pycurl.TIMEOUT, 3)    try:        c.perform()    except pycurl.error:        http_code = 500        http_total_time = 999    else:        http_code = c.getinfo(pycurl.HTTP_CODE)        http_total_time = c.getinfo(pycurl.TOTAL_TIME)    return http_code, http_total_time    #根据curl返回值，exporter显示的数据统计 -- 统计每个状态的总数deff count_metric(url):    http_code, http_total_time = test_website(url)    if http_code >= 100 and http_code < 200 :        url_http_code.labels(1xx，url).inc()    elif http_code >= 200 and http_code < 300 :        url_http_code.labels(2xx，url).inc()    elif http_code >= 300 and http_code < 400 :        url_http_code.labels(3xx，url).inc()    elif http_code >= 400 and http_code < 500 :        url_http_code.labels(四xx，url).inc()    else:        url_http_code.labels(5xx，url).inc()    if http_total_time < 1 :        url_http_request_time.labels('1',url).inc()    elif http_total_time < 2 :        url_http_request_time.labels('2',url).inc()    elif http_total_time < 3 :        url_http_request_time.labels('3',url).inc()    else :        url_http_request_time.labels('+Inf',url).inc()    http_request_total.labels(url).inc()    #线程控制，每隔5秒执行一次curlll urldef count_threads(url):    while True:        t = threading.Thread(target=count_metric,args=(url,))        t.setDaemon(True)        t.start()        time.sleep(5)#为每个需要监控的域名设置一个过程if __name__ == '__main__':    start_http_server(9091)    server_list = [            'www.baidu.com',            'www.qq.com',            'blog.csdn.net',            'github.com',            'google.com'            ]    threads = []    for url in server_list:        t = threading.Thread(target=count_threads,args=(url,))        threads.append(t)    for thread in threads:        thread.setDaemon(True)        thread.start()    thread.join()

坑：prometheus不会提醒metrics的名字，主动复制：curl http://10.0.0.111:19091/metrics--------------------------------------------------------------------------------------

计算exportermetrics的比率

#自定义exporterter-counter##状态码是500个数url_http_code_total{code="5xx",url="10.0.0.111:55555/a.txt"} #http___request_total{url="10.0.0.111:55555/a.txt"}-#错误率deltata(url_http_code_total{code="5xx",url="10.0.0.111:55555/a.txt}[1m] / on(url) group_left delta(http_request_total{url="10.0.0.111:55555/a.txt}[1m]#http 如果code的每分钟增长率出现5xx，这表明irate有问题。(http_request_total[1m])#显示预期时间的比例，如只显示小于1秒，deltata占总次数的比例(url_http_request_time_total{le="1"}[1m] / on(url) group_left delta(http_request_total[1m]).

用一个例子演示会更清晰