Prometheus笔记（一）metric type

Prometheus客户端库提供四种核心度量标准类型。这些目前仅在客户端库中区分（以启用针对特定类型的使用而定制的API）和有线协议。 Prometheus服务器尚未使用类型信息，并将所有数据展平为无类型时间序列。(本文所有示例代码都是使用go来举例的)

1、Counter

计数器是表示单个单调递增计数器的累积量，其值只能增加或在重启时重置为零。例如，您可以使用计数器来表示服务的总请求数，已完成的任务或错误总数。不要使用计数器来监控可能减少的值。例如，不要使用计数器来处理当前正在运行的进程数，而应该用Gauge。

counter主要有两个方法：

//将counter值加1.
Inc()
// 将指定值加到counter值上，如果指定值< 0会panic.
Add(float64)

1.1 Counter

一般 metric 容器使用的步骤都是：

1、初始化一个metric容器

2、Register注册容器

3、向容器中添加值

使用举例：

//step1:初始一个counter
pushCounter := prometheus.NewCounter(prometheus.CounterOpts{Name: "repository_pushes", // 注意: 没有help字符串
})
err := prometheus.Register(pushCounter) // 会返回一个错误.
if err != nil {fmt.Println("Push counter couldn't be registered, no counting will happen:", err)return
}// Try it once more, this time with a help string.
pushCounter = prometheus.NewCounter(prometheus.CounterOpts{Name: "repository_pushes",Help: "Number of pushes to external repository.",
})//setp2： 注册容器
err = prometheus.Register(pushCounter)
if err != nil {fmt.Println("Push counter couldn't be registered AGAIN, no counting will happen:", err)return
}pushComplete := make(chan struct{})
// TODO: Start a goroutine that performs repository pushes and reports
// each completion via the channel.
for range pushComplete {//step3:向容器中写入值pushCounter.Inc()
}

输出：

Push counter couldn't be registered, no counting will happen: descriptor Desc{fqName: "repository_pushes", help: "", constLabels: {}, variableLabels: []} is invalid: empty help string

1.2 CounterVec

CounterVec是一组counter，这些计数器具有相同的描述，但它们的变量标签具有不同的值。如果要计算按各种维度划分的相同内容（例如，响应代码和方法分区的HTTP请求数），则使用此方法。使用NewCounterVec创建实例。

//step1:初始化一个容器
httpReqs := prometheus.NewCounterVec(prometheus.CounterOpts{Name: "http_requests_total",Help: "How many HTTP requests processed, partitioned by status code and HTTP method.",},[]string{"code", "method"},
)
//step2:注册容器
prometheus.MustRegister(httpReqs)httpReqs.WithLabelValues("404", "POST").Add(42)// If you have to access the same set of labels very frequently, it
// might be good to retrieve the metric only once and keep a handle to
// it. But beware of deletion of that metric, see below!
//step3:向容器中写入值，主要调用容器的方法如Inc()或者Add()方法
m := httpReqs.WithLabelValues("200", "GET")
for i := 0; i < 1000000; i++ {m.Inc()
}
// Delete a metric from the vector. If you have previously kept a handle
// to that metric (as above), future updates via that handle will go
// unseen (even if you re-create a metric with the same label set
// later).
httpReqs.DeleteLabelValues("200", "GET")
// Same thing with the more verbose Labels syntax.
httpReqs.Delete(prometheus.Labels{"method": "GET", "code": "200"})

2、Gauge

2.1 Gauge

Gauge可以用来存放一个可以任意变大变小的数值，通常用于测量值，例如温度或当前内存使用情况，或者运行的goroutine数量

主要有以下四个方法

// 将Gauge中的值设为指定值.
Set(float64)
// 将Gauge中的值加1.
Inc()
// 将Gauge中的值减1.
Dec()
// 将指定值加到Gauge中的值上。(指定值可以为负数)
Add(float64)
// 将指定值从Gauge中的值减掉。(指定值可以为负数)
Sub(float64)

示例代码（实时统计CPU的温度）：

//step1：初始化容器
cpuTemprature := prometheus.NewGauge(prometheus.GaugeOpts{Name:      "CPU_Temperature",Help:      "the temperature of CPU",
})
//step2:注册容器
prometheus.MustRegister(cpuTemprature)
//定时获取cpu温度并且写入到容器
func(){tem = getCpuTemprature()//step3:向容器中写入值。调用容器的方法cpuTemprature.Set(tem)
}

2.2 GaugeVec

假设你要一次性统计四个cpu的温度，这个时候就适合使用GaugeVec了。

cpusTemprature := prometheus.NewGaugeVec(prometheus.GaugeOpts{Name:      "CPUs_Temperature",Help:      "the temperature of CPUs.",},[]string{// Which cpu temperature?"cpuName",},
)
prometheus.MustRegister(cpusTemprature)cpusTemprature.WithLabelValues("cpu1").Set(temperature1)
cpusTemprature.WithLabelValues("cpu2").Set(temperature2)
cpusTemprature.WithLabelValues("cpu3").Set(temperature3)

3、Summary

Summary从事件或样本流中捕获单个观察，并以类似于传统汇总统计的方式对其进行汇总：1。观察总和，2。观察计数，3。排名估计。典型的用例是观察请求延迟。默认情况下，Summary提供延迟的中位数。

temps := prometheus.NewSummary(prometheus.SummaryOpts{Name:       "pond_temperature_celsius",Help:       "The temperature of the frog pond.",Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001},
})// Simulate some observations.
for i := 0; i < 1000; i++ {temps.Observe(30 + math.Floor(120*math.Sin(float64(i)*0.1))/10)
}// Just for demonstration, let's check the state of the summary by
// (ab)using its Write method (which is usually only used by Prometheus
// internally).
metric := &dto.Metric{}
temps.Write(metric)
fmt.Println(proto.MarshalTextString(metric))

4、Histogram

主要用于表示一段时间范围内对数据进行采样，（通常是请求持续时间或响应大小），并能够对其指定区间以及总数进行统计，通常我们用它计算分位数的直方图。

temps := prometheus.NewHistogram(prometheus.HistogramOpts{Name:    "pond_temperature_celsius",Help:    "The temperature of the frog pond.", // Sorry, we can't measure how badly it smells.Buckets: prometheus.LinearBuckets(20, 5, 5),  // 5 buckets, each 5 centigrade wide.
})// Simulate some observations.
for i := 0; i < 1000; i++ {temps.Observe(30 + math.Floor(120*math.Sin(float64(i)*0.1))/10)
}// Just for demonstration, let's check the state of the histogram by
// (ab)using its Write method (which is usually only used by Prometheus
// internally).
metric := &dto.Metric{}
temps.Write(metric)
fmt.Println(proto.MarshalTextString(metric))

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二、参考资料

[1] https://godoc.org/github.com/prometheus/client_golang/prometheus
[2] https://prometheus.io/docs/introduction/overview/