Prometheus开发中间件Exporter过程详解

站长资源 2024/11/24 佚名

6 1538 6

DDR爱好者之家 Design By 杰米

Prometheus 为开发这提供了客户端工具，用于为自己的中间件开发Exporter，对接Prometheus 。

目前支持的客户端

Go
Java
Python
Ruby

以go为例开发自己的Exporter

依赖包的引入

工程结构

[root@node1 data]# tree exporter/
exporter/
├── collector
│ └── node.go
├── go.mod
└── main.go

引入依赖包

require (
  github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
  github.com/modern-go/reflect2 v1.0.1 // indirect
  github.com/prometheus/client_golang v1.1.0
    //借助gopsutil 采集主机指标
  github.com/shirou/gopsutil v0.0.0-20190731134726-d80c43f9c984
)

main.go

package main

import (
  "cloud.io/exporter/collector"
  "fmt"
  "github.com/prometheus/client_golang/prometheus"
  "github.com/prometheus/client_golang/prometheus/promhttp"
  "net/http"
)

func init() {
   //注册自身采集器
  prometheus.MustRegister(collector.NewNodeCollector())
}
func main() {
  http.Handle("/metrics", promhttp.Handler())
  if err := http.ListenAndServe(":8080", nil); err != nil {
    fmt.Printf("Error occur when start server %v", err)
  }
}

为了能看清结果我将默认采集器注释,位置registry.go

func init() {
  //MustRegister(NewProcessCollector(ProcessCollectorOpts{}))
  //MustRegister(NewGoCollector())
}

/collector/node.go

代码中涵盖了Counter、Gauge、Histogram、Summary四种情况，一起混合使用的情况，具体的说明见一下代码中。

package collector

import (
  "github.com/prometheus/client_golang/prometheus"
  "github.com/shirou/gopsutil/host"
  "github.com/shirou/gopsutil/mem"
  "runtime"
  "sync"
)

var reqCount int32
var hostname string
type NodeCollector struct {
  requestDesc  *prometheus.Desc  //Counter
  nodeMetrics   nodeStatsMetrics //混合方式 
  goroutinesDesc *prometheus.Desc  //Gauge
  threadsDesc  *prometheus.Desc //Gauge
  summaryDesc  *prometheus.Desc //summary
  histogramDesc *prometheus.Desc  //histogram
  mutex     sync.Mutex
}
//混合方式数据结构
type nodeStatsMetrics []struct {
  desc  *prometheus.Desc
  eval  func(*mem.VirtualMemoryStat) float64
  valType prometheus.ValueType
}

//初始化采集器
func NewNodeCollector() prometheus.Collector {
  host,_:= host.Info()
  hostname = host.Hostname
  return &NodeCollector{
    requestDesc: prometheus.NewDesc(
      "total_request_count",
      "请求数",
      []string{"DYNAMIC_HOST_NAME"}, //动态标签名称
      prometheus.Labels{"STATIC_LABEL1":"静态值可以放在这里","HOST_NAME":hostname}),
    nodeMetrics: nodeStatsMetrics{
      {
        desc: prometheus.NewDesc(
          "total_mem",
          "内存总量",
          nil, nil),
        valType: prometheus.GaugeValue,
        eval: func(ms *mem.VirtualMemoryStat) float64 { return float64(ms.Total) / 1e9 },
      },
      {
        desc: prometheus.NewDesc(
          "free_mem",
          "内存空闲",
          nil, nil),
        valType: prometheus.GaugeValue,
        eval: func(ms *mem.VirtualMemoryStat) float64 { return float64(ms.Free) / 1e9 },
      },

    },
    goroutinesDesc:prometheus.NewDesc(
      "goroutines_num",
      "协程数.",
      nil, nil),
    threadsDesc: prometheus.NewDesc(
      "threads_num",
      "线程数",
      nil, nil),
    summaryDesc: prometheus.NewDesc(
      "summary_http_request_duration_seconds",
      "summary类型",
      []string{"code", "method"},
      prometheus.Labels{"owner": "example"},
    ),
    histogramDesc: prometheus.NewDesc(
      "histogram_http_request_duration_seconds",
      "histogram类型",
      []string{"code", "method"},
      prometheus.Labels{"owner": "example"},
    ),
  }
}

// Describe returns all descriptions of the collector.
//实现采集器Describe接口
func (n *NodeCollector) Describe(ch chan<- *prometheus.Desc) {
  ch <- n.requestDesc
  for _, metric := range n.nodeMetrics {
    ch <- metric.desc
  }
  ch <- n.goroutinesDesc
  ch <- n.threadsDesc
  ch <- n.summaryDesc
  ch <- n.histogramDesc
}
// Collect returns the current state of all metrics of the collector.
//实现采集器Collect接口,真正采集动作
func (n *NodeCollector) Collect(ch chan<- prometheus.Metric) {
  n.mutex.Lock()
  ch <- prometheus.MustNewConstMetric(n.requestDesc,prometheus.CounterValue,0,hostname)
  vm, _ := mem.VirtualMemory()
  for _, metric := range n.nodeMetrics {
    ch <- prometheus.MustNewConstMetric(metric.desc, metric.valType, metric.eval(vm))
  }

  ch <- prometheus.MustNewConstMetric(n.goroutinesDesc, prometheus.GaugeValue, float64(runtime.NumGoroutine()))

  num, _ := runtime.ThreadCreateProfile(nil)
  ch <- prometheus.MustNewConstMetric(n.threadsDesc, prometheus.GaugeValue, float64(num))

  //模拟数据
  ch <- prometheus.MustNewConstSummary(
    n.summaryDesc,
    4711, 403.34,
    map[float64]float64{0.5: 42.3, 0.9: 323.3},
    "200", "get",
  )

  //模拟数据
  ch <- prometheus.MustNewConstHistogram(
      n.histogramDesc,
      4711, 403.34,
      map[float64]uint64{25: 121, 50: 2403, 100: 3221, 200: 4233},
      "200", "get",
    )
  n.mutex.Unlock()
}

执行的结果http://127.0.0.1:8080/metrics

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。

Prometheus,开发,中间件,Exporter

DDR爱好者之家 Design By 杰米

广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站资源来自互联网收集,仅供用于学习和交流,请遵循相关法律法规,本站一切资源不代表本站立场,如有侵权、后门、不妥请联系本站删除！

DDR爱好者之家 Design By 杰米

评论“Prometheus开发中间件Exporter过程详解”

暂无评论...

Prometheus开发中间件Exporter过程详解

python代码实现猜拳小游戏

Django 权限管理(permissions)与用户组(group)详解

评论“Prometheus开发中间件Exporter过程详解”

《魔兽世界》大逃杀！60人新游玩模式《强袭风暴》3月21日上线

更新日志

友情链接