如何使用OpenCensus测量普罗米修斯仪表？

Question

我正在尝试找到一种在Golang中使用OpenCencus测量普罗米修斯量规的方法。目标是跟踪活动会话的数量。因此，该值可以增加和减少，也可以在服务器重新启动时重置为0。

他们有一个https://opencensus.io/quickstart/go/metrics/的例子，但我不能将任何与Gauge和relate关联到0。

你能建议我应该使用哪个测量和视图来测量仪表，哪些可以增加、减少和重置为0吗？

Answer 1

https://opencensus.io/stats/view/

我没有尝试过，但是LastValue可能会(!?)转换为普罗米修斯压力计。

Count给出了测量的次数，并产生一个(递增的)计数器。所以，这对你没有帮助。

唯一的替代方案是Sum和Distribution。

如果LastValue不能产生量规，您可能需要使用Distribution。

更新：LastValue == Gauge

黑掉了给出的例子：

package main

import (
    "context"
    "fmt"
    "log"
    "math/rand"
    "net/http"
    "os"
    "time"

    "contrib.go.opencensus.io/exporter/prometheus"
    "go.opencensus.io/stats"
    "go.opencensus.io/stats/view"
    "go.opencensus.io/tag"
)

var (
    MLatencyMs = stats.Float64("latency", "The latency in milliseconds", "ms")
)
var (
    KeyMethod, _ = tag.NewKey("method")
)

func main() {

    port := os.Getenv("PORT")
    if port == "" {
        port = "8080"
    }

    view1 := &view.View{
        Name:        "dist",
        Measure:     MLatencyMs,
        Description: "The dist of the latencies",
        TagKeys:     []tag.Key{KeyMethod},
        Aggregation: view.Distribution(0, 10, 100, 1000, 10000, 100000),
    }

    view2 := &view.View{
        Name:        "last",
        Measure:     MLatencyMs,
        Description: "The last of the latencies",
        TagKeys:     []tag.Key{KeyMethod},
        Aggregation: view.LastValue(),
    }

    if err := view.Register(view1, view2); err != nil {
        log.Fatalf("Failed to register the views: %v", err)
    }

    pe, err := prometheus.NewExporter(prometheus.Options{
        Namespace: "distlast",
    })
    if err != nil {
        log.Fatalf("Failed to create the Prometheus stats exporter: %v", err)
    }

    go func() {
        mux := http.NewServeMux()
        mux.Handle("/metrics", pe)
        log.Fatal(http.ListenAndServe(fmt.Sprintf(":%s", port), mux))
    }()

    rand.Seed(time.Now().UnixNano())
    ctx := context.Background()

    for {
        n := rand.Intn(100)
        log.Printf("[loop] n=%d\n", n)
        stats.Record(ctx, MLatencyMs.M(float64(time.Duration(n))))
        time.Sleep(1 * time.Second)
    }

}

然后go run .会产生如下结果：

2020/10/15 14:03:25 [loop] n=77
2020/10/15 14:03:26 [loop] n=62
2020/10/15 14:03:27 [loop] n=48
2020/10/15 14:03:28 [loop] n=76
2020/10/15 14:03:29 [loop] n=20
2020/10/15 14:03:30 [loop] n=46
2020/10/15 14:03:31 [loop] n=47
2020/10/15 14:03:32 [loop] n=64
2020/10/15 14:03:33 [loop] n=15
2020/10/15 14:03:34 [loop] n=8

和localhost:8080/metrics收益率的指标：

# HELP distlast_dist The dist of the latencies
# TYPE distlast_dist histogram
distlast_dist_bucket{method="",le="10"} 1
distlast_dist_bucket{method="",le="100"} 10
distlast_dist_bucket{method="",le="1000"} 10
distlast_dist_bucket{method="",le="10000"} 10
distlast_dist_bucket{method="",le="100000"} 10
distlast_dist_bucket{method="",le="+Inf"} 10
distlast_dist_sum{method=""} 463.00000000000006
distlast_dist_count{method=""} 10
# HELP distlast_last The last of the latencies
# TYPE distlast_last gauge
distlast_last{method=""} 8

NOTE distlast_last的值为8，对应于n=8，distlast_dist_sum的值为463。