Esempio n. 1
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func (r *registry) giveMetric(m *dto.Metric) {
	m.Reset()
	select {
	case r.metricPool <- m:
	default:
	}
}
Esempio n. 2
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func (h *histogram) Write(out *dto.Metric) error {
	his := &dto.Histogram{}
	buckets := make([]*dto.Bucket, len(h.upperBounds))

	his.SampleSum = proto.Float64(math.Float64frombits(atomic.LoadUint64(&h.sumBits)))
	his.SampleCount = proto.Uint64(atomic.LoadUint64(&h.count))
	var count uint64
	for i, upperBound := range h.upperBounds {
		count += atomic.LoadUint64(&h.counts[i])
		buckets[i] = &dto.Bucket{
			CumulativeCount: proto.Uint64(count),
			UpperBound:      proto.Float64(upperBound),
		}
	}
	his.Bucket = buckets
	out.Histogram = his
	out.Label = h.labelPairs
	return nil
}
Esempio n. 3
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func populateMetric(
	t ValueType,
	v float64,
	labelPairs []*dto.LabelPair,
	m *dto.Metric,
) error {
	m.Label = labelPairs
	switch t {
	case CounterValue:
		m.Counter = &dto.Counter{Value: proto.Float64(v)}
	case GaugeValue:
		m.Gauge = &dto.Gauge{Value: proto.Float64(v)}
	case UntypedValue:
		m.Untyped = &dto.Untyped{Value: proto.Float64(v)}
	default:
		return fmt.Errorf("encountered unknown type %v", t)
	}
	return nil
}
Esempio n. 4
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func (s *summary) Write(out *dto.Metric) error {
	sum := &dto.Summary{}
	qs := make([]*dto.Quantile, 0, len(s.objectives))

	s.bufMtx.Lock()
	s.mtx.Lock()
	// Swap bufs even if hotBuf is empty to set new hotBufExpTime.
	s.swapBufs(time.Now())
	s.bufMtx.Unlock()

	s.flushColdBuf()
	sum.SampleCount = proto.Uint64(s.cnt)
	sum.SampleSum = proto.Float64(s.sum)

	for _, rank := range s.sortedObjectives {
		var q float64
		if s.headStream.Count() == 0 {
			q = math.NaN()
		} else {
			q = s.headStream.Query(rank)
		}
		qs = append(qs, &dto.Quantile{
			Quantile: proto.Float64(rank),
			Value:    proto.Float64(q),
		})
	}

	s.mtx.Unlock()

	if len(qs) > 0 {
		sort.Sort(quantSort(qs))
	}
	sum.Quantile = qs

	out.Summary = sum
	out.Label = s.labelPairs
	return nil
}
Esempio n. 5
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func (s *constSummary) Write(out *dto.Metric) error {
	sum := &dto.Summary{}
	qs := make([]*dto.Quantile, 0, len(s.quantiles))

	sum.SampleCount = proto.Uint64(s.count)
	sum.SampleSum = proto.Float64(s.sum)

	for rank, q := range s.quantiles {
		qs = append(qs, &dto.Quantile{
			Quantile: proto.Float64(rank),
			Value:    proto.Float64(q),
		})
	}

	if len(qs) > 0 {
		sort.Sort(quantSort(qs))
	}
	sum.Quantile = qs

	out.Summary = sum
	out.Label = s.labelPairs

	return nil
}
Esempio n. 6
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func (h *constHistogram) Write(out *dto.Metric) error {
	his := &dto.Histogram{}
	buckets := make([]*dto.Bucket, 0, len(h.buckets))

	his.SampleCount = proto.Uint64(h.count)
	his.SampleSum = proto.Float64(h.sum)

	for upperBound, count := range h.buckets {
		buckets = append(buckets, &dto.Bucket{
			CumulativeCount: proto.Uint64(count),
			UpperBound:      proto.Float64(upperBound),
		})
	}

	if len(buckets) > 0 {
		sort.Sort(buckSort(buckets))
	}
	his.Bucket = buckets

	out.Histogram = his
	out.Label = h.labelPairs

	return nil
}
Esempio n. 7
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func (cm *CallbackMetric) Write(m *dto.Metric) error {
	m.Untyped = &dto.Untyped{Value: proto.Float64(cm.callback())}
	return nil
}
Esempio n. 8
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func ExampleExpvarCollector() {
	expvarCollector := prometheus.NewExpvarCollector(map[string]*prometheus.Desc{
		"memstats": prometheus.NewDesc(
			"expvar_memstats",
			"All numeric memstats as one metric family. Not a good role-model, actually... ;-)",
			[]string{"type"}, nil,
		),
		"lone-int": prometheus.NewDesc(
			"expvar_lone_int",
			"Just an expvar int as an example.",
			nil, nil,
		),
		"http-request-map": prometheus.NewDesc(
			"expvar_http_request_total",
			"How many http requests processed, partitioned by status code and http method.",
			[]string{"code", "method"}, nil,
		),
	})
	prometheus.MustRegister(expvarCollector)

	// The Prometheus part is done here. But to show that this example is
	// doing anything, we have to manually export something via expvar.  In
	// real-life use-cases, some library would already have exported via
	// expvar what we want to re-export as Prometheus metrics.
	expvar.NewInt("lone-int").Set(42)
	expvarMap := expvar.NewMap("http-request-map")
	var (
		expvarMap1, expvarMap2                             expvar.Map
		expvarInt11, expvarInt12, expvarInt21, expvarInt22 expvar.Int
	)
	expvarMap1.Init()
	expvarMap2.Init()
	expvarInt11.Set(3)
	expvarInt12.Set(13)
	expvarInt21.Set(11)
	expvarInt22.Set(212)
	expvarMap1.Set("POST", &expvarInt11)
	expvarMap1.Set("GET", &expvarInt12)
	expvarMap2.Set("POST", &expvarInt21)
	expvarMap2.Set("GET", &expvarInt22)
	expvarMap.Set("404", &expvarMap1)
	expvarMap.Set("200", &expvarMap2)
	// Results in the following expvar map:
	// "http-request-count": {"200": {"POST": 11, "GET": 212}, "404": {"POST": 3, "GET": 13}}

	// Let's see what the scrape would yield, but exclude the memstats metrics.
	metricStrings := []string{}
	metric := dto.Metric{}
	metricChan := make(chan prometheus.Metric)
	go func() {
		expvarCollector.Collect(metricChan)
		close(metricChan)
	}()
	for m := range metricChan {
		if strings.Index(m.Desc().String(), "expvar_memstats") == -1 {
			metric.Reset()
			m.Write(&metric)
			metricStrings = append(metricStrings, metric.String())
		}
	}
	sort.Strings(metricStrings)
	for _, s := range metricStrings {
		fmt.Println(strings.TrimRight(s, " "))
	}
	// Output:
	// label:<name:"code" value:"200" > label:<name:"method" value:"GET" > untyped:<value:212 >
	// label:<name:"code" value:"200" > label:<name:"method" value:"POST" > untyped:<value:11 >
	// label:<name:"code" value:"404" > label:<name:"method" value:"GET" > untyped:<value:13 >
	// label:<name:"code" value:"404" > label:<name:"method" value:"POST" > untyped:<value:3 >
	// untyped:<value:42 >
}