Prometheus Exporter - 直接检测与自定义收集器
我目前正在为遥测网络应用程序编写一个 Prometheus 导出器。
我已阅读此处的文档“编写导出器”，虽然我了解实现自定义收集器以避免竞争条件的用例，但我不确定我的用例是否适合直接检测。
基本上，网络指标通过网络设备通过 gRPC 进行流式传输，因此我的导出器只需接收它们，而不必有效地抓取它们。
我使用以下代码直接检测：
我使用 promauto 包声明我的指标以保持代码紧凑：
package metrics
import (
"github.com/lucabrasi83/prom-high-obs/proto/telemetry"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
var (
cpu5Sec = promauto.NewGaugeVec(
prometheus.GaugeOpts{
Name: "cisco_iosxe_iosd_cpu_busy_5_sec_percentage",
Help: "The IOSd daemon CPU busy percentage over the last 5 seconds",
},
[]string{"node"},
)
下面是我如何简单地设置 gRPC 协议缓冲区解码消息的指标值：
cpu5Sec.WithLabelValues(msg.GetNodeIdStr()).Set(float64(val))
最后，这是我的主循环，它基本上处理我感兴趣的指标的遥测 gRPC 流：
for {
req, err := stream.Recv()
if err == io.EOF {
return nil
}
if err != nil {
logging.PeppaMonLog(
"error",
fmt.Sprintf("Error while reading client %v stream: %v", clientIPSocket, err))
return err
}
data := req.GetData()
msg := &telemetry.Telemetry{}
err = proto.Unmarshal(data, msg)
if err != nil {
log.Fatalln(err)
}
if !logFlag {
logging.PeppaMonLog(
"info",
fmt.Sprintf(
"Telemetry Subscription Request Received - Client %v - Node %v - YANG Model Path %v",
clientIPSocket, msg.GetNodeIdStr(), msg.GetEncodingPath(),
),
)
}
}
}
我使用 Grafana 作为前端，到目前为止，在关联 Prometheus 公开的指标与直接在设备上检查指标时，还没有看到任何特定的差异。
所以我想了解这是否遵循 Prometheus 最佳实践，或者我仍然应该采用自定义收集器路线。
浮云间1回答
-
凤凰求蛊
您没有遵循最佳实践，因为您正在使用您链接到的文章所警告的全局指标。使用您当前的实现，在设备断开连接后（或者更准确地说，直到您的导出器重新启动），您的仪表板将永远显示 CPU 指标的一些任意且恒定的值。相反，RPC 方法应该维护一组本地指标，并在方法返回后将其删除。这样，当设备断开连接时，设备的指标就会从抓取输出中消失。这是执行此操作的一种方法。它使用包含当前活动指标的地图。每个映射元素都是一个特定流的一组指标（我理解它对应于一个设备）。一旦流结束，该条目就会被删除。package mainimport ( "sync" "github.com/prometheus/client_golang/prometheus")// Exporter is a prometheus.Collector implementation.type Exporter struct { // We need some way to map gRPC streams to their metrics. Using the stream // itself as a map key is simple enough, but anything works as long as we // can remove metrics once the stream ends. sync.Mutex Metrics map[StreamServer]*DeviceMetrics}type DeviceMetrics struct { sync.Mutex CPU prometheus.Metric}// Globally defined descriptions are fine.var cpu5SecDesc = prometheus.NewDesc( "cisco_iosxe_iosd_cpu_busy_5_sec_percentage", "The IOSd daemon CPU busy percentage over the last 5 seconds", []string{"node"}, nil, // constant labels)// Collect implements prometheus.Collector.func (e *Exporter) Collect(ch chan<- prometheus.Metric) { // Copy current metrics so we don't lock for very long if ch's consumer is // slow. var metrics []prometheus.Metric e.Lock() for _, deviceMetrics := range e.Metrics { deviceMetrics.Lock() metrics = append(metrics, deviceMetrics.CPU, ) deviceMetrics.Unlock() } e.Unlock() for _, m := range metrics { if m != nil { ch <- m } }}// Describe implements prometheus.Collector.func (e *Exporter) Describe(ch chan<- *prometheus.Desc) { ch <- cpu5SecDesc}// Service is the gRPC service implementation.type Service struct { exp *Exporter}func (s *Service) RPCMethod(stream StreamServer) (*Response, error) { deviceMetrics := new(DeviceMetrics) s.exp.Lock() s.exp.Metrics[stream] = deviceMetrics s.exp.Unlock() defer func() { // Stop emitting metrics for this stream. s.exp.Lock() delete(s.exp.Metrics, stream) s.exp.Unlock() }() for { req, err := stream.Recv() // TODO: handle error var msg *Telemetry = parseRequest(req) // Your existing code that unmarshals the nested message. var ( metricField *prometheus.Metric metric prometheus.Metric ) switch msg.GetEncodingPath() { case CpuYANGEncodingPath: metricField = &deviceMetrics.CPU metric = prometheus.MustNewConstMetric( cpu5SecDesc, prometheus.GaugeValue, ParsePBMsgCpuBusyPercent(msg), // func(*Telemetry) float64 "node", msg.GetNodeIdStr(), ) default: continue } deviceMetrics.Lock() *metricField = metric deviceMetrics.Unlock() } return nil, &Response{}}
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