examples/random/main.go

// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// A simple example exposing fictional RPC latencies with different types of
// random distributions (uniform, normal, and exponential) as Prometheus
// metrics.
package main

import (
	"flag"
	"log"
	"math"
	"math/rand"
	"net/http"
	"strconv"
	"time"

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

type metrics struct {
	rpcDurations          *prometheus.SummaryVec
	rpcDurationsHistogram prometheus.Histogram
}

func NewMetrics(reg prometheus.Registerer, normMean, normDomain float64) *metrics {
	m := &metrics{
		// Create a summary to track fictional inter service RPC latencies for three
		// distinct services with different latency distributions. These services are
		// differentiated via a "service" label.
		rpcDurations: prometheus.NewSummaryVec(
			prometheus.SummaryOpts{
				Name:       "rpc_durations_seconds",
				Help:       "RPC latency distributions.",
				Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001},
			},
			[]string{"service"},
		),
		// The same as above, but now as a histogram, and only for the
		// normal distribution. The histogram features both conventional
		// buckets as well as sparse buckets, the latter needed for the
		// experimental native histograms (ingested by a Prometheus
		// server v2.40 with the corresponding feature flag
		// enabled). The conventional buckets are targeted to the
		// parameters of the normal distribution, with 20 buckets
		// centered on the mean, each half-sigma wide. The sparse
		// buckets are always centered on zero, with a growth factor of
		// one bucket to the next of (at most) 1.1. (The precise factor
		// is 2^2^-3 = 1.0905077...)
		rpcDurationsHistogram: prometheus.NewHistogram(prometheus.HistogramOpts{
			Name:                        "rpc_durations_histogram_seconds",
			Help:                        "RPC latency distributions.",
			Buckets:                     prometheus.LinearBuckets(normMean-5*normDomain, .5*normDomain, 20),
			NativeHistogramBucketFactor: 1.1,
		}),
	}
	reg.MustRegister(m.rpcDurations)
	reg.MustRegister(m.rpcDurationsHistogram)
	return m
}

func main() {
	var (
		addr              = flag.String("listen-address", ":8080", "The address to listen on for HTTP requests.")
		uniformDomain     = flag.Float64("uniform.domain", 0.0002, "The domain for the uniform distribution.")
		normDomain        = flag.Float64("normal.domain", 0.0002, "The domain for the normal distribution.")
		normMean          = flag.Float64("normal.mean", 0.00001, "The mean for the normal distribution.")
		oscillationPeriod = flag.Duration("oscillation-period", 10*time.Minute, "The duration of the rate oscillation period.")
	)

	flag.Parse()

	// Create a non-global registry.
	reg := prometheus.NewRegistry()

	// Create new metrics and register them using the custom registry.
	m := NewMetrics(reg, *normMean, *normDomain)
	// Add Go module build info.
	reg.MustRegister(collectors.NewBuildInfoCollector())

	start := time.Now()

	oscillationFactor := func() float64 {
		return 2 + math.Sin(math.Sin(2*math.Pi*float64(time.Since(start))/float64(*oscillationPeriod)))
	}

	// Periodically record some sample latencies for the three services.
	go func() {
		for {
			v := rand.Float64() * *uniformDomain
			m.rpcDurations.WithLabelValues("uniform").Observe(v)
			time.Sleep(time.Duration(100*oscillationFactor()) * time.Millisecond)
		}
	}()

	go func() {
		for {
			v := (rand.NormFloat64() * *normDomain) + *normMean
			m.rpcDurations.WithLabelValues("normal").Observe(v)
			// Demonstrate exemplar support with a dummy ID. This
			// would be something like a trace ID in a real
			// application.  Note the necessary type assertion. We
			// already know that rpcDurationsHistogram implements
			// the ExemplarObserver interface and thus don't need to
			// check the outcome of the type assertion.
			m.rpcDurationsHistogram.(prometheus.ExemplarObserver).ObserveWithExemplar(
				v, prometheus.Labels{"dummyID": strconv.Itoa(rand.Intn(100000))},
			)
			time.Sleep(time.Duration(75*oscillationFactor()) * time.Millisecond)
		}
	}()

	go func() {
		for {
			v := rand.ExpFloat64() / 1e6
			m.rpcDurations.WithLabelValues("exponential").Observe(v)
			time.Sleep(time.Duration(50*oscillationFactor()) * time.Millisecond)
		}
	}()

	// Expose the registered metrics via HTTP.
	http.Handle("/metrics", promhttp.HandlerFor(
		reg,
		promhttp.HandlerOpts{
			// Opt into OpenMetrics to support exemplars.
			EnableOpenMetrics: true,
			// Pass custom registry
			Registry: reg,
		},
	))
	log.Fatal(http.ListenAndServe(*addr, nil))
}