From b90660ce37782424aa0ea0b9acef3da103ff6d14 Mon Sep 17 00:00:00 2001 From: yunnysunny Date: Mon, 7 Oct 2024 21:54:53 +0800 Subject: [PATCH] =?UTF-8?q?Prometheus=E6=95=99=E7=A8=8B=E7=BB=AD=E5=86=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- text/14_node_log_and_monitor.md | 95 +++++-- text/Pasted image 20240818180030.png | Bin 86896 -> 0 bytes text/images/prometheus_time_series.drawio | 253 ++++++++++++++++++ text/images/prometheus_time_series.drawio.png | Bin 0 -> 87961 bytes text/images/promethues_datasource.png.md | 0 5 files changed, 319 insertions(+), 29 deletions(-) delete mode 100644 text/Pasted image 20240818180030.png create mode 100644 text/images/prometheus_time_series.drawio create mode 100644 text/images/prometheus_time_series.drawio.png delete mode 100644 text/images/promethues_datasource.png.md diff --git a/text/14_node_log_and_monitor.md b/text/14_node_log_and_monitor.md index 95df024..b44799d 100644 --- a/text/14_node_log_and_monitor.md +++ b/text/14_node_log_and_monitor.md @@ -627,13 +627,14 @@ http.createServer((req, res) => { ```javascript const client = require('prom-client'); +const { commonLabels, commonLabelNames } = require('../config'); const counter = new client.Counter({ name: 'req_count', help: 'http request count', + labelNames: ['path', 'status', ...commonLabelNames], }); -exports.addReqCount = function () { - counter.inc(); // Increment by 1 - console.log('add one'); +exports.addReqCount = function (path, status) { + counter.inc({ ...commonLabels, path, status }); // Increment by 1 }; ``` @@ -770,32 +771,34 @@ nodejs_version_info{instance="127.0.0.1:3001", job="nodejs", major="20", minor=" 其中 instance job major 等类键值对的数据,在 Promethues 中称之为 Lable,最后面那个 1 是当前这条 metric 记录的值。 +如果输入 node_version_info 表达式后回车没有出现任何值,你可以通过打开 http://localhost:9090/targets 连接来看一下被收集的 Endpoint 有没有 Error 信息打印出来。 +![](images/prometheus_targets.png) ->如果输入 node_version_info 表达式后回车没有出现任何值,你可以通过打开 http://localhost:9090/targets 连接来看一下被收集的 Endpoint 有没有 Error 信息打印出来。 ->![](images/prometheus_targets.png) ->正常情况下,每行 targets 记录的 Error 列应该是空的。 +**图 14.3.1.2** + +正常情况下,每行 targets 记录的 Error 列应该是空的。 接着打开 http://localhost:3000/login ,输入用户名密码 `admin` `secret` 即可进入。然后依次选择左侧菜单 **Connection** -> **Data Source** ,然后点击按钮 **Add Data Source**,接着会提供一系列的数据源供给选择,我们选择 Promethues 即可。最后是 Prometheus 的连接配置,我们在 `Prometheus server URL` 栏填入 `http://localhost:9090` ,然后点击 **Save & test** 按钮,正常情况下会提示 `✔ Successfully queried the Prometheus API.` 。 ![](images/prometheus_data_source.png) -**图 14.3.1.2 添加 prometheus 数据源** +**图 14.3.1.3 添加 prometheus 数据源** 然后我们来添加一个面板将指标数据呈现出来,重新回到左侧菜单,选择 Dashboards ,然后点击按钮 Create Dashboard ,显示的操作方式中选择 Import a dashboard: ![](images/import-dashboard.png) -**图 14.3.1.3 选择导入面板** +**图 14.3.1.4 选择导入面板** 在展示的 Find and import dashboards for common applications at [grafana.com/dashboards](https://grafana.com/grafana/dashboards/) 输入框中写入 11159,并点击 **Load** 按钮。 ![](images/input_imported_dashoboard_id.png) -**图 14.3.1.4 输入面板 id** +**图 14.3.1.5 输入面板 id** `1159` 是 grafana.com 上公开的模板 id,具体说明可以参见 [NodeJS Application Dashboard | Grafana Labs](https://grafana.com/grafana/dashboards/11159-nodejs-application-dashboard/),我们将使用这个模板来对 **代码 14.2.2.1** 采集的数据做图标展示。最后我们需要绑定一下面板关联的数据源,在下拉框 prometheus 输入框中选择我们刚才创建的数据源: ![](images/bind_grafana_data_source.png) -**图 14.3.1.5 绑定数据源** +**图 14.3.1.6 绑定数据源** 点击上图的 Import 按钮后,我们就初步完成了报表展示了,会长成这个样子: ![](images/dashboard_grafana_init.png) -**图 14.3.1.6 配置初始化完成后展示的面板** +**图 14.3.1.7 配置初始化完成后展示的面板** 目前我们仅仅演示了一个服务,正常生产环境的服务数可不止一个,有可能有十几个、几十个,甚至更多,而我们在从上图中的 Instance 下拉框中进行筛选是一个很困难的事情。还记得我们改造过的 **代码14.3.1.1** 不,现在它能派上用场了。 @@ -819,15 +822,15 @@ exports.commonLabelNames = Object.keys(exports.commonLabels); ![](images/grafana_variables.png) -**图 14.3.1.7** +**图 14.3.1.8** 现在我们要添加一个 `namespace` 变量,点击 **New variable** 按钮。 ![](images/namespace_variable.png) -**图 14.3.1.8 添加 namespace 变量** +**图 14.3.1.9 添加 namespace 变量** -表单项中 name 输入框我们输入 namespace ,这样我们就新建了一个变量名字,叫 namespace;Lable 输入框填入的 namespace 值,将会导致在 **图 14.3.1.6 ** 中新增一个下拉框,且标记为 namespace,这里你也可以将其改为任何字符,比如说说改成中文名字 `集群`。 +表单项中 name 输入框我们输入 namespace ,这样我们就新建了一个变量名字,叫 namespace;Lable 输入框填入的 namespace 值,将会导致在 **图 14.3.1.6 ** 中新增一个下拉框,且标记为 namespace,这里你也可以将其改为任何字符,比如说说改成中文名字 `集群`。 Query options 区域是这里配置的核心区域,首先在 Data source 区域选择好之前创建好的 Promethues 数据源。下面的 Query 表单中,Query type 选择 Label values,代表我们将从 Prometheus 数据中的 label 属性中提取数据;Labels 选择 namespace ,代表我们使用数据中 label 名字为 namespace 的值进行提取;Metric 选择 node_version_info ,代表我们只从 node_version_info 中提取 label 名字为 namespace 的值。 @@ -835,7 +838,7 @@ Query options 区域是这里配置的核心区域,首先在 Data source 区 ![](images/filter_label_variable.png) -**图 14.3.1.9 筛选 Lable 值** +**图 14.3.1.10 筛选 Lable 值** 我们增加一个 `namespace = $namespace` 的表达式,就能够实现在指定 `namespace` 值下筛选 `serverName` Label 值的能力。对于这个表单时来说等号前面代表名字为 `namespace` 的 Prometheus Label,等号后面的代表前面我们定义的 `namespace` 变量。 @@ -843,13 +846,13 @@ Query options 区域是这里配置的核心区域,首先在 Data source 区 ![](images/variables_ordered.png) -**图 14.3.1.10 调整顺序后的变量** +**图 14.3.1.11 调整顺序后的变量** 上图中 namespace 和 serverName 上面标识了 ⚠️,代表当前变量没有被其他变量引用,但是我们刚才通过设置过滤表达式,已经将所有变量关联起来了,这属于误报,你可以通过点击变量列表左下角按钮 **Show dependencies**,即可查看依赖关系,正常情况下你看到的依赖关系如下图所示: ![](images/variables_deps.png) -**图 14.3.1.11 变量依赖关系** +**图 14.3.1.12 变量依赖关系** 如果你看到的依赖关系没有形成上述依赖链的形式,代表上述的配置中哪个地方是有问题的,需要重新检查一遍。 @@ -859,7 +862,7 @@ Query options 区域是这里配置的核心区域,首先在 Data source 区 ![](images/variables_selectors.png) -**图 14.3.1.12 级联下拉框** +**图 14.3.1.13 级联下拉框** #### 14.3.2 收集自定义指标 @@ -868,23 +871,19 @@ Query options 区域是这里配置的核心区域,首先在 Data source 区 ```javascript const SAMPLES = [20, 50, 80, 80, 100, 100, 100, 120, 120, 140, 160]; const SAMPLES_LEN = SAMPLES.length; +const STATUS = [200, 200, 200, 200, 304, 488, 510, 511]; +const STATUS_LEN = STATUS.length; http.createServer((req, res) => { const duration = SAMPLES[Math.floor(Math.random() * SAMPLES_LEN)]; const begin = Date.now(); setTimeout(function () { - const url = req.url; - console.log('url', url); + const url = req.url;rr + console.log('url', url, req.headers['user-agent']); const path = url.split('?')[0]; - addReqCount(path); + + res.statusCode = STATUS[Math.floor(Math.random() * STATUS_LEN)]; + addReqCount(path, res.statusCode); collectDuration(path, Date.now() - begin); - if (req.url === '/metrics') { - client.register.metrics().then(function (str) { - res.end(str); - }).catch(function (err) { - res.end(err); - }); - return; - } res.end(JSON.stringify({ url, method: req.method, @@ -895,7 +894,45 @@ http.createServer((req, res) => { **代码 14.3.2.1 http请求指标采集** +其中 `addReqCount` 函数的定义参见 **代码 14.2.2.3**,`collectDuration` 函数的定义参见 **代码 14.2.2.4** 。 + +要想制作自定义 grafana 图标,我们得首先了解 Prometheus 的基本概念。我们点击 **图 14.3.1.2** 中 nodejs 对应的 endpoint 链接,打开的页面中会包含类似于如下内容输出: + +``` +# HELP req_count http request count +# TYPE req_count counter +req_count{serverName="chapter14",namespace="default",path="/c",status="200"} 3 +req_count{serverName="chapter14",namespace="default",path="/c",status="511"} 2 +req_count{serverName="chapter14",namespace="default",path="/a",status="200"} 4 +req_count{serverName="chapter14",namespace="default",path="/a",status="304"} 2 +req_count{serverName="chapter14",namespace="default",path="/c",status="510"} 3 + +# HELP req_duration request duration +# TYPE req_duration gauge +req_duration{serverName="chapter14",namespace="default",path="/c"} 94 +req_duration{serverName="chapter14",namespace="default",path="/a"} 110 +req_duration{serverName="chapter14",namespace="default",path="/b"} 126 +``` + +**输出 13.3.2.1** + +由于上述输出全都是一些离散的点,理解起来有些难度,我们将其转成平面图形来进行分析: + +![](images/prometheus_time_series.drawio.png) + +**图 14.3.2.1 Prometheus 基本数据结构图示** + +我们在 **代码 14.3.1.2** 中会看到里面有一个 `scrape_interval` 参数,设定的值是 15s,代表 Prometheus 默认会每隔 15s,去 **图 14.3.1.2** 中定义的各个 endpoint 上抓取数据。如果给 **输出 13.3.2.1** 绘制一张示意图的话,那么这里面两个相邻点的时间间隔应该为 15s。 + +我们用绿色框标识的几个点,是位于 `[t1,t4]` 时间段内的 `req_duration` 指标的一个数据集,这个数据集被称为 **范围向量(Range Vector)** 。 + +我们抽取范围向量中某一个时间点的数据集,就像我们在上图红色框中框选的那样,我们抽取 t2 时间点上的 `req_duration` 指标的数据集,这个数据集被称为 **瞬时向量(Instant Vector)**。 + +如果我们将数据集范围继续缩小,只取其中一个点,也就是在瞬时向量中添加 lable 属性来精确筛选出一个点,这个点被称之为 **标量(Scalar)**。 + + grafana 使用 promql 来绘制图表,我们在 **图 14.3.1.7** 中看到的各个图标,都是基于 promql 语句查询绘制的界面。最简单的 promql 就是直接写指标名字(例如 `req_count`) ,它对应的数据是这个指标最新的瞬时向量的内容。 +需要注意,我们 grafana 只支持瞬时向量和标量,不支持范围向量,但是这并不代表范围向量是没有用的。 ### 示例代码 本章节示例代码可以从这里找到 https://github.com/yunnysunny/nodebook-sample/tree/master/chapter14 diff --git a/text/Pasted image 20240818180030.png b/text/Pasted image 20240818180030.png deleted file mode 100644 index e826173929f743263039e3f02a370e85653c549f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 86896 zcmcG$cUV(P*Efv)h$6>=2vUv(DG`toAR6WjT*<2Gny_TG~8>+5CW*IvD{wupV? z?jjY3F0Sfv?gk1Xbn-{Wi}ASRE%UycmQ#``%L-POF40jm2dQN6_k@?=?03(zGcOn5 zFTXyvvzLw#I`{U)tBXT3>^-%#i8cJpGh0X#Yuo=E50Bva{RiloY-!(}!+BflP19%+ 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