开发自己的Prometheus Exporter、实现自定义指标

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Prometheus Exporter基础知识#

Prometheus Exporter的概念、工作原理#

 Prometheus Exporter是一个用来收集和暴露指标数据的工具，通过与Prometheus监控系统一起使用。它的结构包括两个组件：Collector和Exporter：

  • Collector：用于从目标应用程序或系统收集指标并将其转化为Prometheus可识别的格式。收集器可以使用Prometheus客户端库来生成指标，并公开HTTP/metrics以便Prometheus Server进行定期调用和拉取指标。

  • Exporter：它会从Collector获取指标数据，并将其转成为Prometheus可读格式。

Prometheus Server：它是一个用于存储和查询指标数据的中心化实践序列数据库服务器。Prometheus Server使用scrape_configs的YAML配置文件，其中指定了每个Exporter的配置信息，包括URL、抓取间隔等。

Prometheus 指标格式及其应用#

Prometheus使用一种简单的文本格式来表示指标数据，即“度量名称{标签名=”标签值”, …} 值 时间戳”这样的格式。例如：

http_request_duration_seconds_bucket{le="0.2"} 150 
http_request_duration_seconds_bucket{le="0.5"} 200
http_request_duration_seconds_bucket{le="0.2"} 150 
http_request_duration_seconds_bucket{le="0.5"} 200
http_request_duration_seconds_bucket{le="0.2"} 150 
http_request_duration_seconds_bucket{le="0.5"} 200

其中，”http_request_duration_seconds_bucket”是度量名称，”le”是标签名，”0.2”、”0.5”是标签值， 150、200是相应的值，时间戳则通常省略不写。

Prometheus指标格式的应用包括：

1.收集指标数据：在Prometheus Exporter中，我们通过Collector来收集指标数据，并将其转换为合适的指标格式。

2.暴露指标数据：Prometheus Exporter会把采集到的指标数据暴露给Prometheus，使得Prometheus能够对其进行监控和分析。

3.查询指标数据：在Prometheus中，我们可以使用PromQL查询语言对指标数据查询和分析，比如计算指标的平均值、最大值、最小值等等。

4.可视化指标：通过Grafana等可视化工具，我们可以将Prometheus收集到的指标数据进行图表展示和监控报警等操作。

总之，Prometheus指标格式是Prometheus监控系统中非常重要的一个概念，它是实现收集、查询、暴露、查询和可视化数据的基础。

Prometheus四种指标类型#

Prometheus定义了四种主要的指标类型：

  • Counter（计数器）：用于表示单调递增的指标，例如请求数等。Counter在每次观测时会增加它所代表的值（通常是一个整数），但不会减少或者重置。

  • Gauge（仪表盘）：用于表示可变化的度量值，例如CPU利用率、内存用量等。Gauge可以增加、减少或重置，代表着当前的状态。

  • Histogram（直方图）：用于表示数据样本的分布情况，例如请求延迟等。Histogram将数据按照桶（bucket）进行划分，并对每个桶内的样本计算出一些统计信息，如样本数量、总和、平均值等。

  • Summary（摘要）：类似于Histogram，也用于表示数据样本的分布情况，但同时展示更多的统计信息，如样本数量、总和、平均值、上分位数、下分位数等。

这些指标类型可以组合使用，以便更好的描述系统运行状态和性能指标。例如使用Counter来记录某个API的请求数，使用Gauge来记录内存使用量，使用Histogram来记录请求延迟分布情况。使用Summary来记录响应时间分布情况。在实际应用中应根据需要选择合适的指标类型来表示监控数据。

Exporter Counter 示例#

import (
 "net/http"
 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)
func main() {
 // 创建一个Counter指标
 counterMetric := prometheus.NewCounter(prometheus.CounterOpts{
  Name: "example_counter", // 指标名称
  Help: "An example counter metric.", // 指标帮助信息
 })
 // 注册指标
 prometheus.MustRegister(counterMetric)
 // 增加指标值
 counterMetric.Inc()
 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())
 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "net/http"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Counter指标
 counterMetric := prometheus.NewCounter(prometheus.CounterOpts{
  Name: "example_counter", // 指标名称
  Help: "An example counter metric.", // 指标帮助信息
 })

 // 注册指标
 prometheus.MustRegister(counterMetric)

 // 增加指标值
 counterMetric.Inc()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "net/http"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Counter指标
 counterMetric := prometheus.NewCounter(prometheus.CounterOpts{
  Name: "example_counter", // 指标名称
  Help: "An example counter metric.", // 指标帮助信息
 })

 // 注册指标
 prometheus.MustRegister(counterMetric)

 // 增加指标值
 counterMetric.Inc()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}

Exporter Gauge示例#

import (
 "net/http"
 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)
func main() {
 // 创建一个Gauge指标
 guageMetric := prometheus.NewGauge(prometheus.GaugeOpts{
  Name: "example_gauge", // 指标名称
  Help: "An example gauge metric.", // 指标帮助信息
 })
 // 注册指标
 prometheus.MustRegister(guageMetric)
 // 设置指标值
 guageMetric.Set(100)
 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())
 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "net/http"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Gauge指标
 guageMetric := prometheus.NewGauge(prometheus.GaugeOpts{
  Name: "example_gauge", // 指标名称
  Help: "An example gauge metric.", // 指标帮助信息
 })

 // 注册指标
 prometheus.MustRegister(guageMetric)

 // 设置指标值
 guageMetric.Set(100)

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "net/http"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Gauge指标
 guageMetric := prometheus.NewGauge(prometheus.GaugeOpts{
  Name: "example_gauge", // 指标名称
  Help: "An example gauge metric.", // 指标帮助信息
 })

 // 注册指标
 prometheus.MustRegister(guageMetric)

 // 设置指标值
 guageMetric.Set(100)

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}

Exporter Histogram示例#

import (
 "math/rand"
 "net/http"
 "time"
 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)
func main() {
 // 创建一个Histogram指标
 histogramMetric := prometheus.NewHistogram(prometheus.HistogramOpts{
  Name:    "example_histogram", // 指标名称
  Help:    "An example histogram metric.", // 指标帮助信息
  Buckets: prometheus.LinearBuckets(0, 10, 10), // 设置桶宽度
 })
 // 注册指标
 prometheus.MustRegister(histogramMetric)
 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   histogramMetric.Observe(rand.Float64() * 100)
  }
 }()
 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())
 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "math/rand"
 "net/http"
 "time"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Histogram指标
 histogramMetric := prometheus.NewHistogram(prometheus.HistogramOpts{
  Name:    "example_histogram", // 指标名称
  Help:    "An example histogram metric.", // 指标帮助信息
  Buckets: prometheus.LinearBuckets(0, 10, 10), // 设置桶宽度
 })

 // 注册指标
 prometheus.MustRegister(histogramMetric)

 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   histogramMetric.Observe(rand.Float64() * 100)
  }
 }()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "math/rand"
 "net/http"
 "time"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Histogram指标
 histogramMetric := prometheus.NewHistogram(prometheus.HistogramOpts{
  Name:    "example_histogram", // 指标名称
  Help:    "An example histogram metric.", // 指标帮助信息
  Buckets: prometheus.LinearBuckets(0, 10, 10), // 设置桶宽度
 })

 // 注册指标
 prometheus.MustRegister(histogramMetric)

 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   histogramMetric.Observe(rand.Float64() * 100)
  }
 }()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}

Exporter Summary示例#

import (
 "math/rand"
 "net/http"
 "time"
 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)
func main() {
 // 创建一个Summary指标
 summaryMetric := prometheus.NewSummary(prometheus.SummaryOpts{
  Name:       "example_summary", // 指标名称
  Help:       "An example summary metric.", // 指标帮助信息
  Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001}, // 设置分位数和偏差
 })
 // 注册指标
 prometheus.MustRegister(summaryMetric)
 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   summaryMetric.Observe(rand.Float64() * 100)
  }
 }()
 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())
 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "math/rand"
 "net/http"
 "time"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Summary指标
 summaryMetric := prometheus.NewSummary(prometheus.SummaryOpts{
  Name:       "example_summary", // 指标名称
  Help:       "An example summary metric.", // 指标帮助信息
  Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001}, // 设置分位数和偏差
 })

 // 注册指标
 prometheus.MustRegister(summaryMetric)

 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   summaryMetric.Observe(rand.Float64() * 100)
  }
 }()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}
import (
 "math/rand"
 "net/http"
 "time"

 "github.com/prometheus/client_golang/prometheus"
 "github.com/prometheus/client_golang/prometheus/promhttp"
)

func main() {
 // 创建一个Summary指标
 summaryMetric := prometheus.NewSummary(prometheus.SummaryOpts{
  Name:       "example_summary", // 指标名称
  Help:       "An example summary metric.", // 指标帮助信息
  Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001}, // 设置分位数和偏差
 })

 // 注册指标
 prometheus.MustRegister(summaryMetric)

 // 定期更新指标值
 go func() {
  for {
   time.Sleep(time.Second)
   summaryMetric.Observe(rand.Float64() * 100)
  }
 }()

 // 创建一个HTTP处理器来暴露指标
 http.Handle("/metrics", promhttp.Handler())

 // 启动Web服务器
 http.ListenAndServe(":8080", nil)
}

配置Exporter 开发环境#

在kubernetes中安装Prometheus#

  • 创建一个prometheus-configmap.yaml文件，用于定义Prometheus配置

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'pushgateway'
        static_configs:
          - targets: ['pushgateway:9091']
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'pushgateway'
        static_configs:
          - targets: ['pushgateway:9091']
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'pushgateway'
        static_configs:
          - targets: ['pushgateway:9091']

该配置为Prometheus提供了一些基本选项，并定义了pushgateway的作业。pushgateway用于从pushgateway实例中收集指标数据。

  • 使用kubectl apply命令创建Prometheus ConfigMap

kubectl apply -f prometheus-configmap.yaml
kubectl apply -f prometheus-configmap.yaml
kubectl apply -f prometheus-configmap.yaml

  • 创建一个prometheus-deployment.yaml文件，用于定义Prometheus Deployment的配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
spec:
  selector:
    matchLabels:
      app: prometheus
  replicas: 1
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - name: prometheus
        image: prom/prometheus:v2.32.0
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        ports:
        - containerPort: 9090
        volumeMounts:  # 挂载ConfigMap中的prometheus.yml配置文件
        - name: config-volume
          mountPath: /etc/prometheus
      volumes:
      - name: config-volume
        configMap:
          name: prometheus-config  #引用第一步创建的prometheus-config ConfigMap
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
spec:
  selector:
    matchLabels:
      app: prometheus
  replicas: 1
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - name: prometheus
        image: prom/prometheus:v2.32.0
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        ports:
        - containerPort: 9090
        volumeMounts:  # 挂载ConfigMap中的prometheus.yml配置文件
        - name: config-volume
          mountPath: /etc/prometheus
      volumes:
      - name: config-volume
        configMap:
          name: prometheus-config  #引用第一步创建的prometheus-config ConfigMap
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
spec:
  selector:
    matchLabels:
      app: prometheus
  replicas: 1
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - name: prometheus
        image: prom/prometheus:v2.32.0
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        ports:
        - containerPort: 9090
        volumeMounts:  # 挂载ConfigMap中的prometheus.yml配置文件
        - name: config-volume
          mountPath: /etc/prometheus
      volumes:
      - name: config-volume
        configMap:
          name: prometheus-config  #引用第一步创建的prometheus-config ConfigMap

  • 创建一个Service对象，在kubernetes集群内部和外部暴露Prometheus服务的访问端点

apiVersion: v1
kind: Service
metadata:
  name: prometheus
spec:
  type: NodePort
  ports:
  - port: 9090
    targetPort: 9090 
  selector:
    app: prometheus
apiVersion: v1
kind: Service
metadata:
  name: prometheus
spec:
  type: NodePort
  ports:
  - port: 9090
    targetPort: 9090 
  selector:
    app: prometheus
apiVersion: v1
kind: Service
metadata:
  name: prometheus
spec:
  type: NodePort
  ports:
  - port: 9090
    targetPort: 9090 
  selector:
    app: prometheus

  • 访问Prometheus UI

kubectl apply get svc
kubectl apply get svc
kubectl apply get svc

http://nodeIP:端口/graph

安装PushGateway#

  • 创建一个pushgateway-deployment.yaml文件，用于定义PushGateway Deployment的配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: pushgateway
spec:
  selector:
    matchLabels:
      app: pushgateway
  replicas: 1
  template:
    metadata:
      labels:
        app: pushgateway
    spec:
      containers:
      - name: pushgateway
        image: prom/pushgateway:v1.5.1
        ports:
        - containerPort: 9091
        args:
        - "--web.listen-address=:9091"
apiVersion: apps/v1
kind: Deployment
metadata:
  name: pushgateway
spec:
  selector:
    matchLabels:
      app: pushgateway
  replicas: 1
  template:
    metadata:
      labels:
        app: pushgateway
    spec:
      containers:
      - name: pushgateway
        image: prom/pushgateway:v1.5.1
        ports:
        - containerPort: 9091
        args:
        - "--web.listen-address=:9091"
apiVersion: apps/v1
kind: Deployment
metadata:
  name: pushgateway
spec:
  selector:
    matchLabels:
      app: pushgateway
  replicas: 1
  template:
    metadata:
      labels:
        app: pushgateway
    spec:
      containers:
      - name: pushgateway
        image: prom/pushgateway:v1.5.1
        ports:
        - containerPort: 9091
        args:
        - "--web.listen-address=:9091"

  • 使用kubectl apply命令创建PushGateway Deployment

kubectl apply -f pushgateway-deployment.yaml
kubectl apply -f pushgateway-deployment.yaml
kubectl apply -f pushgateway-deployment.yaml

  • 创建一个pushgateway-service.yaml文件，用于定义pushgateway service配置

apiVersion: v1
kind: Service
metadata:
  name: pushgateway
spec:
  type: NodePort
  ports:
  - port: 9091
    targetPort: 9091
  selector:
    app: pushgateway
apiVersion: v1
kind: Service
metadata:
  name: pushgateway
spec:
  type: NodePort
  ports:
  - port: 9091
    targetPort: 9091
  selector:
    app: pushgateway
apiVersion: v1
kind: Service
metadata:
  name: pushgateway
spec:
  type: NodePort
  ports:
  - port: 9091
    targetPort: 9091
  selector:
    app: pushgateway

  • 使用kubectl apply命令创建PushGateway Service

kubectl apply -f pushgateway-service.yaml
kubectl apply -f pushgateway-service.yaml
kubectl apply -f pushgateway-service.yaml

  • 在Prometheus配置文件中添加Pushgateway作为目录，以便Promethazine可以获取Pushgateway中的指标数据(在第一步prometheus-configmap.yaml中已加过如下配置、此步跳过)。

scrape_configs:
  - job_name: 'pushgateway'
    static_configs:
      - targets: ['pushgateway:9091']
scrape_configs:
  - job_name: 'pushgateway'
    static_configs:
      - targets: ['pushgateway:9091']
scrape_configs:
  - job_name: 'pushgateway'
    static_configs:
      - targets: ['pushgateway:9091']

  • 访问PushGateway

  • Prometheus UI中查看pushgateway

安装Grafana并连接Prometheus#

  • 创建grafana.yaml用于定于grafana的配置

kind: Deployment
metadata:
  name: grafana-dp
spec:
  selector:
    matchLabels:
      app: grafana-dp
  replicas: 1
  template:
    metadata:
      labels:
        app: grafana-dp
    spec:
      containers:
        - name: grafana
          image: grafana/grafana
          imagePullPolicy: IfNotPresent
          ports:
          - containerPort: 3000
---
apiVersion: v1
kind: Service
metadata:
  name: grafana-svc
spec:
  type: NodePort
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    app: grafana-dp
kind: Deployment
metadata:
  name: grafana-dp
spec:
  selector:
    matchLabels:
      app: grafana-dp
  replicas: 1
  template:
    metadata:
      labels:
        app: grafana-dp
    spec:
      containers:
        - name: grafana
          image: grafana/grafana
          imagePullPolicy: IfNotPresent
          ports:
          - containerPort: 3000

---
apiVersion: v1
kind: Service
metadata:
  name: grafana-svc
spec:
  type: NodePort
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    app: grafana-dp
kind: Deployment
metadata:
  name: grafana-dp
spec:
  selector:
    matchLabels:
      app: grafana-dp
  replicas: 1
  template:
    metadata:
      labels:
        app: grafana-dp
    spec:
      containers:
        - name: grafana
          image: grafana/grafana
          imagePullPolicy: IfNotPresent
          ports:
          - containerPort: 3000

---
apiVersion: v1
kind: Service
metadata:
  name: grafana-svc
spec:
  type: NodePort
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    app: grafana-dp

  • 创建grafana deployment和service

kubectl apply -f grafana.yaml
kubectl get svc
kubectl apply -f grafana.yaml
kubectl get svc
kubectl apply -f grafana.yaml
kubectl get svc

用户名admin密码admin

  • 添加Prometheus数据源

监测Go/Python编写的HTTP Server#

https://github.com/prometheus/client_golang

https://prometheus.io/docs/instrumenting/clientlibs/

https://prometheus.io/docs/tutorials/instrumenting_http_server_in_go/

使用client_golang库监测HTTP server#

package main
import (
   "fmt"
   "net/http"
   "github.com/prometheus/client_golang/prometheus"
   "github.com/prometheus/client_golang/prometheus/promhttp"
)
var pingCounter = prometheus.NewCounter(
   prometheus.CounterOpts{
       Name: "ping_request_count",
       Help: "No of request handled by Ping handler",
   },
)
func ping(w http.ResponseWriter, req *http.Request) {
   pingCounter.Inc()
   fmt.Fprintf(w, "pong")
}
func main() {
   prometheus.MustRegister(pingCounter)
   http.HandleFunc("/ping", ping)
   http.Handle("/metrics", promhttp.Handler())
   http.ListenAndServe(":8090", nil)
}
package main

import (
   "fmt"
   "net/http"

   "github.com/prometheus/client_golang/prometheus"
   "github.com/prometheus/client_golang/prometheus/promhttp"
)

var pingCounter = prometheus.NewCounter(
   prometheus.CounterOpts{
       Name: "ping_request_count",
       Help: "No of request handled by Ping handler",
   },
)

func ping(w http.ResponseWriter, req *http.Request) {
   pingCounter.Inc()
   fmt.Fprintf(w, "pong")
}

func main() {
   prometheus.MustRegister(pingCounter)

   http.HandleFunc("/ping", ping)
   http.Handle("/metrics", promhttp.Handler())
   http.ListenAndServe(":8090", nil)
}
package main

import (
   "fmt"
   "net/http"

   "github.com/prometheus/client_golang/prometheus"
   "github.com/prometheus/client_golang/prometheus/promhttp"
)

var pingCounter = prometheus.NewCounter(
   prometheus.CounterOpts{
       Name: "ping_request_count",
       Help: "No of request handled by Ping handler",
   },
)

func ping(w http.ResponseWriter, req *http.Request) {
   pingCounter.Inc()
   fmt.Fprintf(w, "pong")
}

func main() {
   prometheus.MustRegister(pingCounter)

   http.HandleFunc("/ping", ping)
   http.Handle("/metrics", promhttp.Handler())
   http.ListenAndServe(":8090", nil)
}

  • 访问metrics接口

 

使用client_python库监测HTTP server#

https://github.com/prometheus/client_python

from flask import Flask
from werkzeug.serving import run_simple
from werkzeug.middleware.dispatcher import DispatcherMiddleware
from prometheus_client import make_wsgi_app
app = Flask(__name__)
app.debug = True
app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
    '/metrics': make_wsgi_app()
})
@app.route('/')
def hello_world():
    return 'Hello World!'
if __name__ == '__main__':
    run_simple('localhost', 5000, app, use_reloader=True, use_debugger=True, use_evalex=True)
from flask import Flask
from werkzeug.serving import run_simple
from werkzeug.middleware.dispatcher import DispatcherMiddleware
from prometheus_client import make_wsgi_app

app = Flask(__name__)
app.debug = True
app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
    '/metrics': make_wsgi_app()
})

@app.route('/')
def hello_world():
    return 'Hello World!'

if __name__ == '__main__':
    run_simple('localhost', 5000, app, use_reloader=True, use_debugger=True, use_evalex=True)
from flask import Flask
from werkzeug.serving import run_simple
from werkzeug.middleware.dispatcher import DispatcherMiddleware
from prometheus_client import make_wsgi_app

app = Flask(__name__)
app.debug = True
app.wsgi_app = DispatcherMiddleware(app.wsgi_app, {
    '/metrics': make_wsgi_app()
})

@app.route('/')
def hello_world():
    return 'Hello World!'

if __name__ == '__main__':
    run_simple('localhost', 5000, app, use_reloader=True, use_debugger=True, use_evalex=True)

  • requirements.txt

Flask==2.3.2
uWSGI==2.0.21
prometheus-client==0.17.0
Flask==2.3.2
uWSGI==2.0.21
prometheus-client==0.17.0
Flask==2.3.2
uWSGI==2.0.21
prometheus-client==0.17.0

  • 启动Web应用程序

pip install uwsgi
uwsgi --http 127.0.0.1:8000 --wsgi-file main.py --callable app
pip install uwsgi
uwsgi --http 127.0.0.1:8000 --wsgi-file main.py --callable app
pip install uwsgi
uwsgi --http 127.0.0.1:8000 --wsgi-file main.py --callable app

  • 访问metrics接口

 

Nginx Exporter开发#

Nginx status简介#

http_stub_status_module时nginx一个模块，它提供了一些简单的指标和状态信息，这些信息可用于监控nginx服务器的监控状态和性能。要启用该模块，需要在nginx配置文件中添加以下内容：

location /nginx_status {
  stub_status on;
  allow 127.0.0.1;
  deny all;
}
location /nginx_status {
  stub_status on;
  allow 127.0.0.1;
  deny all;
}
location /nginx_status {
  stub_status on;
  allow 127.0.0.1;
  deny all;
}

此配置将使用nginx在nginx_status上公开stub_status信息。这些信息只允许本地主机（即127.0.0.1）上访问，并拒绝来自其他主机上的访问请求。然后，您可以使用curl等工具向nginx服务器发送HTTP GET请求，以获取当前的状态信息。例如，以下命令将获取位于localhost上的nginx服务器的状态信息：

curl http://localhost/nginx_status
curl http://localhost/nginx_status
curl http://localhost/nginx_status

此命令将返回像这样的响应：

Active connections: 1
server accepts handled requests
10 10 10
Reading: 0 Writing: 1 Waiting: 0
Active connections: 1
server accepts handled requests
10 10 10
Reading: 0 Writing: 1 Waiting: 0
Active connections: 1
server accepts handled requests
10 10 10
Reading: 0 Writing: 1 Waiting: 0

  • Active connectons：当前活跃的连接数。

  • Server Accepts handled requests: 表示从启动到现在一共处理过的连接数，其中accepts表示接收的连接数，handled表示已经处理完成的连接数（可能包括已经关闭的连接数），requests表示已经处理完成的请求数；

  • Reading: 正在读取客户端的请求的连接数；

  • Writing：正在向客户端发送响应的连接数；

  • Waiting：正在等待请求处理的连接数。

除了以上信息，可以通过第三方模块来扩展stub_status模块的功能，例如：ngx_http_substitutions_filter_module、lua-nginx-module。

Nginx Exporter开发#

项目源码：https://gitee.com/KubeSec/nginx-exporter

  • /metrics

Exporter部署和测试#

cd nginx-exporter
docker build -t nginx-exporter:latest .
kubectl apply -f manifests/install.yaml
cd nginx-exporter
docker build -t nginx-exporter:latest .
kubectl apply -f manifests/install.yaml
cd nginx-exporter
docker build -t nginx-exporter:latest .
kubectl apply -f manifests/install.yaml

  • 访问/metrics

http://nodeIP:nodePort/metrics

  • 在Prometheus中配置我们的nginx-exporter

    scrape_configs:
      - job_name: 'nginx-exporter'
        static_configs:
          - targets: ['nginx-exporter-svc:2113']
    scrape_configs:
      - job_name: 'nginx-exporter'
        static_configs:
          - targets: ['nginx-exporter-svc:2113']
    scrape_configs:
      - job_name: 'nginx-exporter'
        static_configs:
          - targets: ['nginx-exporter-svc:2113']

  • 在Prometheus UI中查看我的 exporter

  • 补充： memcached_exporter_example项目源码https://gitee.com/KubeSec/memcached_exporter

 

使用Grafana可视化指标#

Grafana图表类型#

  • Time Series：时序数据，用于显示时间序列数据。可以使用折线图、面截图、柱状图等形式来呈现。

  • Stat: 可以用于显示数据的统计信息，例如平均值、总和、最大值、最小值等。

  • Table: 用以表格形式显示数据。可以将数据按行或列进行排序，并支持筛选和聚合操作。

 PromQL 查询和分析指标数据#

PromQL（Prometheus Query Language）是由Prometheus开发的一种灵活的查询语言，用于收集的时间序列中提取、聚合和分析Metric。

up: 返回当前所有Exporter的状态， 1表示健康正在运行，0表示不可访问或异常。sum(nginx_status_requests{job=”nginx-exporter”, instance=”nginx-exporter-svc:2113″})要检索Nginx服务器中当前链接的数量。

创建Nginx Exporter Dashboard#

Grafana导入manifests/nginx-exporter-dashboard.json

 

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