From 05967b59277ade9339ecef2c0c9d1216112b1bba Mon Sep 17 00:00:00 2001 From: yunnysunny Date: Sun, 1 Dec 2024 12:30:17 +0800 Subject: [PATCH] =?UTF-8?q?grafana=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 --- gen_word.sh | 25 ++++++ text/14_node_log_and_monitor.md | 84 +++++++++++++++++++- text/images/add_visual.png | Bin 0 -> 10411 bytes text/images/input_visualization_express.png | Bin 0 -> 203551 bytes text/images/input_visulization_express.png | Bin 0 -> 63889 bytes 5 files changed, 105 insertions(+), 4 deletions(-) create mode 100644 gen_word.sh create mode 100644 text/images/add_visual.png create mode 100644 text/images/input_visualization_express.png create mode 100644 text/images/input_visulization_express.png diff --git a/gen_word.sh b/gen_word.sh new file mode 100644 index 0000000..79e16ef --- /dev/null +++ b/gen_word.sh @@ -0,0 +1,25 @@ +pandoc 00_preface.md \ +01_node_introduce.md \ +02_node_javascript.md \ +03_node_basic.md \ +04_node_npm.md \ +05_node_database.md \ +06_node_express_basic.md \ +07_node_express_advance.md \ +08_node_unit_test.md \ +09_node_production.md \ +10_node_addon.md \ +11_node_optimization.md \ +12_node_web_security.md \ +13_node_web_worker.md \ +14_node_log_and_monitor.md \ +15_micro_service.md \ +16_openapi_doc.md \ +a1_node_utils.md \ +a2_bibliography.md \ +a3_convention.md \ +a4_node_http.md \ +a5_node_multi_versions.md \ +a6_node_native_addon_config.md \ +a7_easy_monitor_setup.md \ +-o output.docx \ No newline at end of file diff --git a/text/14_node_log_and_monitor.md b/text/14_node_log_and_monitor.md index c239f91..43a8062 100644 --- a/text/14_node_log_and_monitor.md +++ b/text/14_node_log_and_monitor.md @@ -922,15 +922,91 @@ req_duration{serverName="chapter14",namespace="default",path="/b"} 126 **图 14.3.2.1 Prometheus 基本数据结构图示** +> 为了构图方便,这里简单认为各个指标中只有 `status` 这一个 lable。 + 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_count` 指标的数据集,这个数据集被称为 **瞬时向量(Instant Vector)** 。`req_count` 这个表达式就是一个瞬时向量,它其实是一个简写,省略了当前时间,如果我们想指定非当前时间的数据需要写 `req_count @ 时间戳`,比如说 `req_count @ 1732458388` 代表 req_count 在 2024-11-24 22:26:28 时间点的数据。因为 `req_count` 上有一个 `status` 的label,这个 lable 有很多可选值(200、500 等),所以这个瞬时向量转化成编程语言的概念的话,就可以认为是一个数组。我们给出下面一组数组来演示这个数组: + +```javascript +[ + 10/*lable为200的req_count值*/, + 1/*lable为500的req_count值*/, + 2/*label为501的req_count值*/ +] +``` + +**代码 14.3.2.2 瞬时向量演示** + +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对应的数值,被称之为 **标量(Scalar)**。比如说 `req_count{status="200"} @ 1732458388`, 它代表 HTTP 响应码为 200 的 req_count 指标在 2024-11-24 22:26:28 时间点的值,也就是 **代码 14.3.2.1** 中的 `10` 这个值。 + +> 标量其实就是一个浮点类型的数值,是没有表达式概念的,表达式`req_count{status="200"} @ 1732458388` 依然是一个瞬时向量,只不过其对应的数组里面只有一个元素,也就是说这个数组长这样 `[10]` 。 + +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)** 。如果将其转化为编程语言中的概念的话,用二维数组更好理解一些,下面是我们对其的数据演示: + +```javascript +[ + [// 时间为t1的瞬时数据,t1=1732458388 + 10/*lable为200的req_count值*/, + 1/*lable为500的req_count值*/, + 2/*label为501的req_count值*/ + ], + [// 时间为t2的瞬时数据, t2=1732458448 + 18/*lable为200的req_count值*/, + 2/*lable为500的req_count值*/, + 3/*label为501的req_count值*/ + ], + [// 时间为t3的瞬时数据,t3=1732458508 + 20/*lable为200的req_count值*/, + 3/*lable为500的req_count值*/, + 3/*label为501的req_count值*/ + ], + [// 时间为t4的瞬时数据,t4=1732458568 + 22/*lable为200的req_count值*/, + 3/*lable为500的req_count值*/, + 4/*label为501的req_count值*/ + ], +] +``` + +**代码 14.3.2.3 范围向量演示** + +`req_count[4m]` 就是一个范围向量的表达式,它表示 req_count 指标在当前时间往前 4 分钟内的所有数据。我们直接使用范围向量的情况很少,一般是作为过渡存在,再次将其转化为瞬时向量。在程序中我们使用 req_count 来表示当前请求的累计总数,这个数字是单调递增的,但是如果我们想知道每秒请求数,可以使用 rate 函数,比如说 rate(req_count[4m]) 就可以计算 4 分钟内每秒请求数,也就是请求速率。根据高中物理公式,`速率 = 距离 / 时间`,我们假定在四分钟内 t1 为第一个采集到的数据,t4 为最后一个采集到的数据,则对于 `status` 为 200 的指标来说,其速率为 + +``` +rate=(22-10)/(4*60)​=0.05 请求/秒 +``` + +**公式 14.3.2.1 速率公式** + +根据上述公式,我们依次带入各个 status 的 req_count 首尾差值,就会得到一个瞬时向量: + +``` +[ + 0.05/*lable为200的req_count速率*/, + 0.008/*lable为500的req_count速率*/, + 0.008/*label为501的req_count速率*/ +] +``` + +**代码 14.3.2.4 应用 rate 函数后生成的瞬时向量** + + 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,不过 grafana 要求这些 promql 必须是瞬时向量。最简单的瞬时向量就是直接写指标名字(例如 `req_count`)。 + +我们在 **图 14.3.1.7** 的右上角位置找到 **Add** 菜单,点击打开后选择 **Visualization**: + +![](images/add_visual.png) + +**图 14.3.2.2 添加 grafana 可视化面板菜单** + +在弹出的界面中输入表达式 `req_count{instance=~"$instance"}` : + +![](images/input_visualization_express.png) -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)**。 +**图 14.3.2.3 填写可视化所需的表达式** -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)**。 +注意这里我们在 lable 筛选的时候用了 =~ ,而不是常用的 = ,这是由于 $instance 这个变量值的特殊性导致的。 - grafana 使用 promql 来绘制图表,我们在 **图 14.3.1.7** 中看到的各个图标,都是基于 promql 语句查询绘制的界面。最简单的 promql 就是直接写指标名字(例如 `req_count`) ,它对应的数据是这个指标最新的瞬时向量的内容。 需要注意,我们 grafana 只支持瞬时向量和标量,不支持范围向量,但是这并不代表范围向量是没有用的。对于计数器类型的指标来说,grafana 也不支持,这时候你可以使用 Prometheus 内置函数将计数器先转化成范围向量,然后再对范围向量内的数值做运算转成一个瞬时向量,比如说 rate 函数就是将范围向量转成瞬时向量的常用操作。 diff --git a/text/images/add_visual.png b/text/images/add_visual.png new file mode 100644 index 0000000000000000000000000000000000000000..f886df187818ee7dae19d65429440d9affe4c3b8 GIT binary patch literal 10411 zcmY*7MRBQ>V_KdI&`^CxC*ufm2 zYHVra#HJ#xNx{p;c^i0}@a9o(t|R|NUY-en{H7oPh=2$H;2TnSW8(jd-{t~f0K)%| z{lD380^tA{Z&LS-Eki;7W4Cz2|B=xf{}=!NW@>1fOZl6yKkc>$F zq^&KkMQBYcwEm6%$f8*LN58teeIp!=uYXm{?>cA)hizwiXqQ63L4HH72b-cE&pSjr+IS_xHmFJ1 z=9T}1_aK%eI0v4SsvoPj698AFJBnc11mcM?c57qzq0VKyhHbVC@K?n_$s{%E#V9tm zOwmG&Ez+@(5i`u07Dl!sP$Z&FDNYSRI+v>8!#zbM5M^{FGJ{gzkh6htCajM>to|C@ zOcHTSKvRyfF(!dkK^BW5`^!t#R?M>Atf`17j7m}EuYWb=j3uV 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