给expvarmon插上数据持久化的“翅膀”

本文永久链接 – https://tonybai.com/2021/04/14/expvarmon-save-and-convert-to-xlsx

1. expvar包与expvarmon

Go在标准库中为暴露Go应用内部指标数据提供了标准的对外接口，这就是expvar包。expvar包通过init函数将内置的expvarHandler(一个标准http HandlerFunc)注册到http包ListenAndServe创建的默认Server上。

// $GOROOT/src/expvar/expvar.go
func init() {
        http.HandleFunc("/debug/vars", expvarHandler)
        Publish("cmdline", Func(cmdline))
        Publish("memstats", Func(memstats))
}

这样如果一个Go应用要想利用expvar默认暴露的内部指标数据，仅需做到两点：

以副作用方式导入expvar包

import _ "expvar"

启动默认HTTP Server

http.ListenAndServe("localhost:8080", nil)

我们来建立的使用expvar包暴露指标的最简单的例子：

// expvar_demo1.go
package main

import (
    _ "expvar"
    "net/http"
)

func main() {
    http.ListenAndServe(":8080", nil)
}

这样expvar包的expvarHandler会自动响应到localhost:8080/debug/vars上的http请求：

$go build expvar_demo1.go
$./expvar_demo1 -w=1 -r=2

$curl localhost:8080/debug/vars
{
"cmdline": ["./expvar_demo1","-w=1","-r=2"],
"memstats": {"Alloc":227088,"TotalAlloc":227088,"Sys":71650320,"Lookups":0,"Mallocs":730,"Frees":13,"HeapAlloc":227088,"HeapSys":66715648,"HeapIdle":65937408,"HeapInuse":778240,"HeapReleased":65937408,"HeapObjects":717,"StackInuse":393216,"StackSys":393216,"MSpanInuse":37536,"MSpanSys":49152,"MCacheInuse":9600,"MCacheSys":16384,"BuckHashSys":3769,"GCSys":3783272,"OtherSys":688879,"NextGC":4473924,"LastGC":0,"PauseTotalNs":0,"PauseNs":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"PauseEnd":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"NumGC":0,"NumForcedGC":0,"GCCPUFraction":0,"EnableGC":true,"DebugGC":false,"BySize":[{"Size":0,"Mallocs":0,"Frees":0},{"Size":8,"Mallocs":14,"Frees":0},{"Size":16,"Mallocs":297,"Frees":0},{"Size":24,"Mallocs":32,"Frees":0},{"Size":32,"Mallocs":20,"Frees":0},{"Size":48,"Mallocs":105,"Frees":0},{"Size":64,"Mallocs":31,"Frees":0},{"Size":80,"Mallocs":9,"Frees":0},{"Size":96,"Mallocs":13,"Frees":0},{"Size":112,"Mallocs":2,"Frees":0},{"Size":128,"Mallocs":7,"Frees":0},{"Size":144,"Mallocs":3,"Frees":0},{"Size":160,"Mallocs":16,"Frees":0},{"Size":176,"Mallocs":5,"Frees":0},{"Size":192,"Mallocs":0,"Frees":0},{"Size":208,"Mallocs":33,"Frees":0},{"Size":224,"Mallocs":3,"Frees":0},{"Size":240,"Mallocs":0,"Frees":0},{"Size":256,"Mallocs":10,"Frees":0},{"Size":288,"Mallocs":8,"Frees":0},{"Size":320,"Mallocs":2,"Frees":0},{"Size":352,"Mallocs":10,"Frees":0},{"Size":384,"Mallocs":24,"Frees":0},{"Size":416,"Mallocs":7,"Frees":0},{"Size":448,"Mallocs":0,"Frees":0},{"Size":480,"Mallocs":1,"Frees":0},{"Size":512,"Mallocs":0,"Frees":0},{"Size":576,"Mallocs":3,"Frees":0},{"Size":640,"Mallocs":3,"Frees":0},{"Size":704,"Mallocs":5,"Frees":0},{"Size":768,"Mallocs":0,"Frees":0},{"Size":896,"Mallocs":7,"Frees":0},{"Size":1024,"Mallocs":7,"Frees":0},{"Size":1152,"Mallocs":10,"Frees":0},{"Size":1280,"Mallocs":4,"Frees":0},{"Size":1408,"Mallocs":1,"Frees":0},{"Size":1536,"Mallocs":0,"Frees":0},{"Size":1792,"Mallocs":5,"Frees":0},{"Size":2048,"Mallocs":1,"Frees":0},{"Size":2304,"Mallocs":2,"Frees":0},{"Size":2688,"Mallocs":2,"Frees":0},{"Size":3072,"Mallocs":0,"Frees":0},{"Size":3200,"Mallocs":0,"Frees":0},{"Size":3456,"Mallocs":0,"Frees":0},{"Size":4096,"Mallocs":4,"Frees":0},{"Size":4864,"Mallocs":0,"Frees":0},{"Size":5376,"Mallocs":1,"Frees":0},{"Size":6144,"Mallocs":1,"Frees":0},{"Size":6528,"Mallocs":0,"Frees":0},{"Size":6784,"Mallocs":0,"Frees":0},{"Size":6912,"Mallocs":0,"Frees":0},{"Size":8192,"Mallocs":1,"Frees":0},{"Size":9472,"Mallocs":0,"Frees":0},{"Size":9728,"Mallocs":0,"Frees":0},{"Size":10240,"Mallocs":8,"Frees":0},{"Size":10880,"Mallocs":0,"Frees":0},{"Size":12288,"Mallocs":0,"Frees":0},{"Size":13568,"Mallocs":0,"Frees":0},{"Size":14336,"Mallocs":0,"Frees":0},{"Size":16384,"Mallocs":0,"Frees":0},{"Size":18432,"Mallocs":0,"Frees":0}]}
}

如果我们不使用http.ListenAndServe建立的默认Server呢？expvar包也提供了相应的方法帮助你在自定义http server以及自定义请求路径上使用expvarHandler，我们看看下面示例：

// expvar_demo2.go
package main

import (
    "expvar"
    "net/http"
)

func main() {
    mux := http.NewServeMux()
    mux.Handle("/mydebug/myvars", expvar.Handler())
    var server = &http.Server{
        Addr:    "localhost:8081",
        Handler: mux,
    }

    server.ListenAndServe()
}

在这个示例中，我们利用http.ServeMux建立了expvarHandler响应的自定义路径(/mydebug/myvars)，并自定义了一个http.Server，这样当expvar_demo2运行起来后，我们就可以在localhost:8081/mydebug/myvars上获取该应用暴露的指标数据了。

通过expvar_demo1的输出结果，我们看到expvar默认将命令行字段和runtime包的MemStats结构暴露给外部。我们也可以自定义要暴露到外部的数据，expvar包提供了常用指标类型的便捷接口以帮助我们更容易的自定义要暴露到外部的数据，看下面示例：

// expvar_demo3.go
package main

import (
    "expvar"
    _ "expvar"
    "net/http"
)

var (
    total *expvar.Int
)

func init() {
    total = expvar.NewInt("TotalRequest")
}

func main() {
    http.Handle("/", http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        total.Add(1)
        w.Write([]byte("hello, go\n"))
    }))
    http.ListenAndServe(":8080", nil)
}

在这个示例中，我们自定义了一个公开指标TotalRequest，用于描述该Server总共处理了多少个请求。我们用*expvar.Int作为TotalRequest的类型，expvar.Int类型提供了并发安全的Add方法，利用该方法我们可以对指标做运算。运行上面示例后，我们就可以获取TotalRequest这个指标了：

$curl localhost:8080/debug/vars
{
"TotalRequest": 2,
... ..
}

expvar包提供了对外的数据接口，但观测方式却是你决定的。图形化的观测方式是对人类友好的，一位名为divan的gopher开发了expvarmon工具，该工具可以在命令行终端以图形化的方式实时展示特定的指标数据的变化，我们可以执行如下命令实时查看应用指标变化；

$expvarmon -ports="http://localhost:8080/debug/vars" -i 1s

命令执行的效果如下：

如果不指定指标，那么expvarmon默认展示上述图中的memstats的几个指标！

expvarmon支持实时获取数据并展示数据的实时变动趋势。但有些时候，我们不仅要看实时趋势，可能还需要将数据存储起来便于事后分析。但expvarmon不支持将数据序列化到磁盘并做历史数据查看和分析。笔者曾提过issue，但作者似乎认为这个项目完成度已经很高了，该项目到2019年后就没有更新了。因此自然也没人理我的issue。我也只能自己动手丰衣足食了。

2. expvarmon的大致原理

要想基于expvarmon二次开发出支持数据持久化的版本，我们首先需要大致弄清楚expvarmon的工作原理，这里我将其工作原理大致总结为下面这幅示意图：

expvarmon执行时的两个命令行标志参数很重要：-ports和-vars。前者决定了expvarmon启动多少个Service(每个Service一个goroutine承载)：

// https://github.com/divan/expvarmon/blob/master/main.go

        for _, port := range ports {
                service := NewService(port, vars)
                data.Services = append(data.Services, service)
        }

后者用于指定expvarmon要实时显示的数据项：

// https://github.com/divan/expvarmon/blob/master/service.go

// NewService returns new Service object.
func NewService(url url.URL, vars []VarName) *Service {
        values := make(map[VarName]*Stack)
        for _, name := range vars { //根据vars建立存储对应var数据的Stack
                values[VarName(name)] = NewStack()
        }
    ... ...
}

expvar定时采集各个目标app的指标数据

// https://github.com/divan/expvarmon/blob/master/service.go

func main() {
    ... ...
    UpdateAll(ui, data)
        for {
                select {
                case <-tick.C:
                        UpdateAll(ui, data)
                case e := <-termui.PollEvents():
                        if e.Type == termui.KeyboardEvent && e.ID == "q" {
                                return
                        }
                        if e.Type == termui.ResizeEvent {
                                ui.Update(*data)
                        }
                }
        }
}

// UpdateAll collects data from expvars and refreshes UI.
func UpdateAll(ui UI, data *UIData) {
        var wg sync.WaitGroup
        for _, service := range data.Services {
                wg.Add(1)
                go service.Update(&wg) // 每个服务单独获取对应port的数据
        }
        wg.Wait()

        data.LastTimestamp = time.Now()

        ui.Update(*data) // 更新并刷新命令行终端ui
}

3. 持久化到csv文件中

大致了解expvarmon的运作原理后，我们就来设计和实现将expvarmon启动后针对每个port得到的指标数据存储到磁盘文件中留待后续分析之用，这里选择持久化到csv文件中，csv文件不仅便于直接打开并肉眼查看，也便于后续转换为其他文件格式，比如：Microsoft的excel文件。

下面是对expvarmon的设计与实现改动点：

增加-w命令行标志参数(布尔型)，如果为true，则持久化获取到的指标数据

// https://github.com/bigwhite/expvarmon/blob/master/main.go
var (
        ... ...
        serialize = flag.Bool("w", false, "Serialize the data into a disk file")
)

在Service结构中增加持久化数据所需字段

// https://github.com/bigwhite/expvarmon/blob/master/service.go

// Service represents constantly updating info about single service.
type Service struct {
        ... ...
        vars    []VarName
        // for serializing the data
        // controlled by cmd option: serialize
        f *os.File
        w *csv.Writer // csv writer
}

vars用于存储该Service对应的指标名；f为文件名；w是创建的csv.Writer结构。

在创建Service的时候，根据-w的值来决定是否创建持久化文件：

// https://github.com/bigwhite/expvarmon/blob/master/service.go
func NewService(url url.URL, vars []VarName) *Service {
    ... ...
    if *serialize {
                f, err := os.Create(s.Name + ".csv")
                if err != nil {
                        panic(err)
                }
                s.f = f
                s.w = csv.NewWriter(f)

                // write first record: category line
                record := []string{"time"}
                for _, v := range vars {
                        record = append(record, string(v))
                }
                s.w.Write(record)
                s.w.Flush()
        }
        ... ...
}

我们看到：当-w为true时，NewService创建了持久化文件，并用Service的Name+.csv后缀为其命名。文件创建成功后，我们将写入第一行csv数据，这一行数据为数据类别，就像下面这样：

// 10.10.195.133:8080.csv
time,mem:memstats.Alloc,mem:memstats.Sys,mem:memstats.HeapAlloc,mem:memstats.HeapInuse,duration:memstats.PauseNs,duration:memstats.PauseTotalNs

除了第一列为时间(time)外，其余列都是以指标名命名的，如：mem:memstats.Alloc。

我们在Service的Update方法中定时写入指标数据

// https://github.com/bigwhite/expvarmon/blob/master/service.go

// Update updates Service info from Expvar variable.
func (s *Service) Update(wg *sync.WaitGroup) {
        ... ...
        if *serialize {
                // serialize the values to csv
                tm := time.Now().Format("2006-01-02 15:04:05")
                values := []string{tm}
                for _, name := range s.vars {
                        values = append(values, s.Value(name))
                }
                s.w.Write(values)
                s.w.Flush()
        }
}

增加Service的Close方法以优雅关闭csv文件

和原expvarmon不同的是，我们二次开发的expvarmon在-w为true时会为每个Service创建一个磁盘文件，这样我们就需要记着在适当的时候优雅的关闭这些csv格式的磁盘文件。

// https://github.com/bigwhite/expvarmon/blob/master/service.go

// Close does some cleanup before service exit
func (s *Service) Close() {
        if *serialize {
                if s.f != nil {
                        s.f.Close()
                }
        }
}

我们在程序退出前通过defer来调用Service的关闭方法：

// https://github.com/bigwhite/expvarmon/blob/master/main.go

func main() {
    ... ...
        // Init UIData
        data := NewUIData(vars)
        for _, port := range ports {
                service := NewService(port, vars)
                data.Services = append(data.Services, service)
        }

        defer func() {
                // close service before program exit
                for _, service := range data.Services {
                        service.Close()
                }
        }()
    ... ...
}

按照上述这几点改造后，我们再执行如下命令：

$expvarmon -ports="http://10.10.195.133:8080/debug/vars" -i 1s -w=true

我们将得到10.10.195.133:8080.csv文件(如果-ports由多个值组成，那么将生成多个.csv文件)，内容如下：

$cat 10.10.195.133:8080.csv
time,mem:memstats.Alloc,mem:memstats.Sys,mem:memstats.HeapAlloc,mem:memstats.HeapInuse,duration:memstats.PauseNs,duration:memstats.PauseTotalNs
2021-04-09 16:55:58,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:55:59,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:00,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:01,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:02,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:03,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:04,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:05,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:06,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:07,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:08,16MB,88MB,16MB,25MB,159µs,1m50s
2021-04-09 16:56:09,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:10,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:11,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:12,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:13,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:14,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:15,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:16,15MB,88MB,15MB,25MB,159µs,1m50s
2021-04-09 16:56:17,15MB,88MB,15MB,25MB,159µs,1m50s
... ...

4. 将csv数据转换为excel图表

csv存储了各个应用暴露给外部的分时指标数据，但要对这些数据进行分析，我们需要将csv中的数据以可视化的形式展示出来，而excel图表是一个不错的选择。

为此，我建立了一个csv2xls的工具项目，专门用来将expvarmon生成的csv文件转换为excel图表。

csv2xls的用法如下：

$./csv2xls -h
Usage of ./csv2xls:
  -col int
        the column which we draw a chart based on, default: 1 (range 0~max-1) (default 1)
  -i string
        the name of csv file
  -o string
        the name of xls file

Examples:
        ./csv2xls -i xxx.csv
        ./csv2xls -i xxx.csv -o yyy.xlsx

csv2xls将csv文件中的数据读取并存储在xls中，并支持基于其中某列数据生成对应的折线图。以上面的10.10.195.133:8080.csv为例，我们通过命令：csv2xls -i 10.10.195.133:8080.csv即可生成如下excel图表文件：

csv2xls使用p著名的excelize(go语言execl操作库)](github.com/360EntSecGroup-Skylar/excelize)生成excel文件。

5. 小结

至此，我们给expvarmon插上数据持久化的“翅膀”的目的算是初步达到了。但是由于app指标数据千变万化，expvarmon使用的byten包又给解析指标数据单位带来了一些复杂性，因此csv2xls还不完善，后续还有很大的改进的空间。

支持公开指标持久化的expvarmon的代码在这里(https://github.com/bigwhite/expvarmon)。
csv2xls的代码在这里(https://github.com/bigwhite/csv2xls)。

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Gopher Daily(Gopher每日新闻)归档仓库 – https://github.com/bigwhite/gopherdaily

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