From 8cc09244a0b3e2bae7d542ba92afbd3cad4ae924 Mon Sep 17 00:00:00 2001 From: zhoushuguang <15951703783@163.com> Date: Sat, 12 Sep 2020 13:08:10 +0800 Subject: [PATCH] Metric (#65) * doc for service metric * doc for service metric * doc for service metric * doc for service metric Co-authored-by: zhoushuguang --- doc/images/datasource.png | Bin 0 -> 73658 bytes doc/images/panel.png | Bin 0 -> 467816 bytes doc/images/prom_up.png | Bin 0 -> 112028 bytes doc/images/prometheus.png | Bin 0 -> 96834 bytes doc/images/qps.png | Bin 0 -> 123649 bytes doc/images/qps_panel.png | Bin 0 -> 101541 bytes doc/images/variables.png | Bin 0 -> 92698 bytes doc/metric.md | 112 ++++++++++++++++++++++++++++++++++++++ 8 files changed, 112 insertions(+) create mode 100644 doc/images/datasource.png create mode 100644 doc/images/panel.png create mode 100644 doc/images/prom_up.png create mode 100644 doc/images/prometheus.png create mode 100644 doc/images/qps.png create mode 100644 doc/images/qps_panel.png create 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+![job状态为up](./images/prom_up.png) + +通过以上几个步骤我们完成了prometheus对shorturl服务的指标监控收集的配置工作,为了演示简单我们进行了手动的配置,在实际的生产环境中一般采用定时更新配置文件或者服务发现的方式来配置监控目标,篇幅有限这里不展开讲解,感兴趣的同学请自行查看相关文档 + +## go-zero监控的指标类型 + +go-zero中目前在http的中间件和rpc的拦截器中添加了对请求指标的监控。 + +主要从请求耗时和请求错误两个维度,请求耗时采用了Histogram指标类型定义了多个Buckets方便进行分位统计,请求错误采用了Counter类型,并在http metric中添加了path标签rpc metric中添加了method标签以便进行细分监控。 + +接下来演示如何查看监控指标: + +首先在命令行多次执行如下命令 + +```go +curl -i "http://localhost:8888/shorten?url=http://www.xiaoheiban.cn" +``` + +打开Prometheus切换到Graph界面,在输入框中输入{path="/shorten"}指令,即可查看监控指标,如下图 + +![查询面板](./images/panel.png) + +我们通过PromQL语法查询过滤path为/shorten的指标,结果中显示了指标名以及指标数值,其中http_server_requests_code_total指标中code值为http的状态码,200表明请求成功,http_server_requests_duration_ms_bucket中对不同bucket结果分别进行了统计,还可以看到所有的指标中都添加了我们配置的默认指标 + +Console界面主要展示了查询的指标结果,Graph界面为我们提供了简单的图形化的展示界面,在实际的生产环境中我们一般使用Grafana做图形化的展示 + +## grafana可视化界面 + +[grafana](https://grafana.com/)是一款可视化工具,功能强大,支持多种数据来源Prometheus、Elasticsearch、Graphite等,安装比较简单请参考[官方文档](https://grafana.com/docs/grafana/latest/),grafana默认端口3000,安装好后再浏览器输入http://localhost:3000/,默认账号和密码都为admin + +下面演示如何基于以上指标进行可视化界面的绘制: + +- 点击左侧边栏Configuration->Data Source->Add data source进行数据源添加,其中HTTP的URL为数据源的地址 + +![datasource](./images/datasource.png) + +- 点击左侧边栏添加dashboard,然后添加Variables方便针对不同的标签进行过滤筛选比如添加app变量用来过滤不同的服务 + +![variables](./images/variables.png) + +- 进入dashboard点击右上角Add panel添加面板,以path维度统计接口的qps + +![qps](./images/qps.png) + +- 最终的效果如下所示,可以通过服务名称过滤不同的服务,面板展示了path为/shorten的qps变化趋势 + +![qps panel](./images/qps_panel.png) + +## 总结 + +以上演示了go-zero中基于prometheus+grafana服务指标监控的简单流程,生产环境中可以根据实际的场景做不同维度的监控分析。现在go-zero的监控指标主要还是针对http和rpc,这对于服务的整体监控显然还是不足的,比如容器资源的监控,依赖的mysql、redis等资源的监控,以及自定义的指标监控等等,go-zero在这方面后续还会持续优化。希望这篇文章能够给您带来帮助

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