2015-02-02 17:14:36 +03:00
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// Copyright 2015 The Prometheus Authors
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2015-01-06 17:35:57 +03:00
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// A simple example exposing fictional RPC latencies with different types of
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// random distributions (uniform, normal, and exponential) as Prometheus
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// metrics.
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package main
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import (
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"flag"
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2020-01-14 21:22:19 +03:00
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"fmt"
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2016-11-02 12:52:31 +03:00
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"log"
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2015-01-06 19:02:26 +03:00
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"math"
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2015-01-06 17:35:57 +03:00
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"math/rand"
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"net/http"
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"time"
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2022-12-13 11:48:40 +03:00
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"git.internal/re/client_golang/prometheus"
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"git.internal/re/client_golang/prometheus/collectors"
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"git.internal/re/client_golang/prometheus/promhttp"
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2015-01-06 17:35:57 +03:00
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)
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2022-10-21 17:47:21 +03:00
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type metrics struct {
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rpcDurations *prometheus.SummaryVec
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rpcDurationsHistogram prometheus.Histogram
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}
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2015-01-06 17:35:57 +03:00
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2022-10-21 17:47:21 +03:00
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func NewMetrics(reg prometheus.Registerer, normMean, normDomain float64) *metrics {
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m := &metrics{
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// Create a summary to track fictional inter service RPC latencies for three
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2021-11-10 18:20:10 +03:00
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// distinct services with different latency distributions. These services are
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// differentiated via a "service" label.
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rpcDurations: prometheus.NewSummaryVec(
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prometheus.SummaryOpts{
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Name: "rpc_durations_seconds",
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Help: "RPC latency distributions.",
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Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001},
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},
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[]string{"service"},
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),
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// The same as above, but now as a histogram, and only for the
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// normal distribution. The histogram features both conventional
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// buckets as well as sparse buckets, the latter needed for the
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// experimental native histograms (ingested by a Prometheus
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// server v2.40 with the corresponding feature flag
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// enabled). The conventional buckets are targeted to the
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// parameters of the normal distribution, with 20 buckets
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// centered on the mean, each half-sigma wide. The sparse
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// buckets are always centered on zero, with a growth factor of
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2022-11-09 19:56:59 +03:00
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// one bucket to the next of (at most) 1.1. (The precise factor
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// is 2^2^-3 = 1.0905077...)
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rpcDurationsHistogram: prometheus.NewHistogram(prometheus.HistogramOpts{
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Name: "rpc_durations_histogram_seconds",
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Help: "RPC latency distributions.",
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2022-10-31 18:30:59 +03:00
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Buckets: prometheus.LinearBuckets(normMean-5*normDomain, .5*normDomain, 20),
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2022-10-27 23:31:38 +03:00
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NativeHistogramBucketFactor: 1.1,
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}),
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}
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reg.MustRegister(m.rpcDurations)
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reg.MustRegister(m.rpcDurationsHistogram)
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return m
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}
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func main() {
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var (
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addr = flag.String("listen-address", ":8080", "The address to listen on for HTTP requests.")
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uniformDomain = flag.Float64("uniform.domain", 0.0002, "The domain for the uniform distribution.")
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normDomain = flag.Float64("normal.domain", 0.0002, "The domain for the normal distribution.")
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normMean = flag.Float64("normal.mean", 0.00001, "The mean for the normal distribution.")
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oscillationPeriod = flag.Duration("oscillation-period", 10*time.Minute, "The duration of the rate oscillation period.")
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)
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flag.Parse()
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// Create a non-global registry.
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reg := prometheus.NewRegistry()
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// Create new metrics and register them using the custom registry.
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m := NewMetrics(reg, *normMean, *normDomain)
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2019-06-06 22:07:33 +03:00
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// Add Go module build info.
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reg.MustRegister(collectors.NewBuildInfoCollector())
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2015-01-06 19:02:26 +03:00
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start := time.Now()
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oscillationFactor := func() float64 {
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return 2 + math.Sin(math.Sin(2*math.Pi*float64(time.Since(start))/float64(*oscillationPeriod)))
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}
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// Periodically record some sample latencies for the three services.
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go func() {
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for {
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v := rand.Float64() * *uniformDomain
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m.rpcDurations.WithLabelValues("uniform").Observe(v)
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time.Sleep(time.Duration(100*oscillationFactor()) * time.Millisecond)
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}
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}()
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go func() {
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for {
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2015-02-23 18:19:01 +03:00
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v := (rand.NormFloat64() * *normDomain) + *normMean
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m.rpcDurations.WithLabelValues("normal").Observe(v)
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2020-01-26 01:40:35 +03:00
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// Demonstrate exemplar support with a dummy ID. This
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// would be something like a trace ID in a real
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// application. Note the necessary type assertion. We
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// already know that rpcDurationsHistogram implements
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// the ExemplarObserver interface and thus don't need to
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2020-01-27 17:46:39 +03:00
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// check the outcome of the type assertion.
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m.rpcDurationsHistogram.(prometheus.ExemplarObserver).ObserveWithExemplar(
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2020-01-14 21:22:19 +03:00
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v, prometheus.Labels{"dummyID": fmt.Sprint(rand.Intn(100000))},
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)
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2015-01-06 19:02:26 +03:00
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time.Sleep(time.Duration(75*oscillationFactor()) * time.Millisecond)
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}
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}()
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2015-01-06 17:35:57 +03:00
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2015-01-06 19:02:26 +03:00
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go func() {
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for {
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2016-11-12 18:36:39 +03:00
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v := rand.ExpFloat64() / 1e6
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m.rpcDurations.WithLabelValues("exponential").Observe(v)
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time.Sleep(time.Duration(50*oscillationFactor()) * time.Millisecond)
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}
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}()
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// Expose the registered metrics via HTTP.
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2020-01-14 21:22:19 +03:00
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http.Handle("/metrics", promhttp.HandlerFor(
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reg,
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2020-01-14 21:22:19 +03:00
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promhttp.HandlerOpts{
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// Opt into OpenMetrics to support exemplars.
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EnableOpenMetrics: true,
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// Pass custom registry
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Registry: reg,
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2020-01-14 21:22:19 +03:00
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},
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))
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2016-11-02 12:52:31 +03:00
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log.Fatal(http.ListenAndServe(*addr, nil))
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2015-01-06 17:35:57 +03:00
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}
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