服务部署在阿里云的K8s上,配置了基于Prometheus的Grafana监控。原本用的是自定义的Metrics接口统计,上报一些字段,后面发现Prometheus自带的监控非常全面好用,适合直接抓取统计,所以做了一些改变。
pip install prometheus-client
# encoding: utf-8 from prometheus_client import Counter, Gauge, Summary from prometheus_client.core import CollectorRegistry from prometheus_client.exposition import choose_encoder class Monitor: def __init__(self): # 注册收集器&最大耗时map self.collector_registry = CollectorRegistry(auto_describe=False) self.request_time_max_map = {} # 接口调用summary统计 self.http_request_summary = Summary(name="http_server_requests_seconds", documentation="Num of request time summary", labelnames=("method", "code", "uri"), registry=self.collector_registry) # 接口最大耗时统计 self.http_request_max_cost = Gauge(name="http_server_requests_seconds_max", documentation="Number of request max cost", labelnames=("method", "code", "uri"), registry=self.collector_registry) # 请求失败次数统计 self.http_request_fail_count = Counter(name="http_server_requests_error", documentation="Times of request fail in total", labelnames=("method", "code", "uri"), registry=self.collector_registry) # 模型预测耗时统计 self.http_request_predict_cost = Counter(name="http_server_requests_seconds_predict", documentation="Seconds of prediction cost in total", labelnames=("method", "code", "uri"), registry=self.collector_registry) # 图片下载耗时统计 self.http_request_download_cost = Counter(name="http_server_requests_seconds_download", documentation="Seconds of download cost in total", labelnames=("method", "code", "uri"), registry=self.collector_registry) # 获取/metrics结果 def get_prometheus_metrics_info(self, handler): encoder, content_type = choose_encoder(handler.request.headers.get('accept')) handler.set_header("Content-Type", content_type) handler.write(encoder(self.collector_registry)) self.reset_request_time_max_map() # summary统计 def set_prometheus_request_summary(self, handler): self.http_request_summary.labels(handler.request.method, handler.get_status(), handler.request.path).observe(handler.request.request_time()) self.set_prometheus_request_max_cost(handler) # 自定义summary统计 def set_prometheus_request_summary_customize(self, method, status, path, cost_time): self.http_request_summary.labels(method, status, path).observe(cost_time) self.set_prometheus_request_max_cost_customize(method, status, path, cost_time) # 失败统计 def set_prometheus_request_fail_count(self, handler, amount=1.0): self.http_request_fail_count.labels(handler.request.method, handler.get_status(), handler.request.path).inc(amount) # 自定义失败统计 def set_prometheus_request_fail_count_customize(self, method, status, path, amount=1.0): self.http_request_fail_count.labels(method, status, path).inc(amount) # 最大耗时统计 def set_prometheus_request_max_cost(self, handler): requset_cost = handler.request.request_time() if self.check_request_time_max_map(handler.request.path, requset_cost): self.http_request_max_cost.labels(handler.request.method, handler.get_status(), handler.request.path).set(requset_cost) self.request_time_max_map[handler.request.path] = requset_cost # 自定义最大耗时统计 def set_prometheus_request_max_cost_customize(self, method, status, path, cost_time): if self.check_request_time_max_map(path, cost_time): self.http_request_max_cost.labels(method, status, path).set(cost_time) self.request_time_max_map[path] = cost_time # 预测耗时统计 def set_prometheus_request_predict_cost(self, handler, amount=1.0): self.http_request_predict_cost.labels(handler.request.method, handler.get_status(), handler.request.path).inc(amount) # 自定义预测耗时统计 def set_prometheus_request_predict_cost_customize(self, method, status, path, cost_time): self.http_request_predict_cost.labels(method, status, path).inc(cost_time) # 下载耗时统计 def set_prometheus_request_download_cost(self, handler, amount=1.0): self.http_request_download_cost.labels(handler.request.method, handler.get_status(), handler.request.path).inc(amount) # 自定义下载耗时统计 def set_prometheus_request_download_cost_customize(self, method, status, path, cost_time): self.http_request_download_cost.labels(method, status, path).inc(cost_time) # 校验是否赋值最大耗时map def check_request_time_max_map(self, uri, cost): if uri not in self.request_time_max_map: return True if self.request_time_max_map[uri] < cost: return True return False # 重置最大耗时map def reset_request_time_max_map(self): for key in self.request_time_max_map: self.request_time_max_map[key] = 0.0
import tornado import tornado.ioloop import tornado.web import tornado.gen from datetime import datetime from tools.monitor import Monitor global g_monitor class ClassifierHandler(tornado.web.RequestHandler): def post(self): # TODO Something you need # work.... # 统计Summary,包括请求次数和每次耗时 g_monitor.set_prometheus_request_summary(self) self.write("OK") class PingHandler(tornado.web.RequestHandler): def head(self): print('INFO', datetime.now(), "/ping Head.") g_monitor.set_prometheus_request_summary(self) self.write("OK") def get(self): print('INFO', datetime.now(), "/ping Get.") g_monitor.set_prometheus_request_summary(self) self.write("OK") class MetricsHandler(tornado.web.RequestHandler): def get(self): print('INFO', datetime.now(), "/metrics Get.") g_monitor.set_prometheus_request_summary(self) # 通过Metrics接口返回统计结果 g_monitor.get_prometheus_metrics_info(self) def make_app(): return tornado.web.Application([ (r"/ping?", PingHandler), (r"/metrics?", MetricsHandler), (r"/work?", ClassifierHandler) ]) if __name__ == "__main__": g_monitor = Monitor() app = make_app() app.listen(port) tornado.ioloop.IOLoop.current().start()
Metrics返回结果实例
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