Пример #1
0
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
}
Пример #2
0
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
}
Пример #3
0
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
}
Пример #4
0
func (d *json2Decoder) more() error {
	var entities []struct {
		BaseLabels model.LabelSet `json:"baseLabels"`
		Docstring  string         `json:"docstring"`
		Metric     struct {
			Type   string          `json:"type"`
			Values json.RawMessage `json:"value"`
		} `json:"metric"`
	}

	if err := d.dec.Decode(&entities); err != nil {
		return err
	}
	for _, e := range entities {
		f := &dto.MetricFamily{
			Name:   proto.String(string(e.BaseLabels[model.MetricNameLabel])),
			Help:   proto.String(e.Docstring),
			Type:   dto.MetricType_UNTYPED.Enum(),
			Metric: []*dto.Metric{},
		}

		d.fams = append(d.fams, f)

		switch e.Metric.Type {
		case "counter", "gauge":
			var values []counter002

			if err := json.Unmarshal(e.Metric.Values, &values); err != nil {
				return fmt.Errorf("could not extract %s value: %s", e.Metric.Type, err)
			}

			for _, ctr := range values {
				f.Metric = append(f.Metric, &dto.Metric{
					Label: protoLabelSet(e.BaseLabels, ctr.Labels),
					Untyped: &dto.Untyped{
						Value: proto.Float64(ctr.Value),
					},
				})
			}

		case "histogram":
			var values []histogram002

			if err := json.Unmarshal(e.Metric.Values, &values); err != nil {
				return fmt.Errorf("could not extract %s value: %s", e.Metric.Type, err)
			}

			for _, hist := range values {
				quants := make([]string, 0, len(values))
				for q := range hist.Values {
					quants = append(quants, q)
				}

				sort.Strings(quants)

				for _, q := range quants {
					value := hist.Values[q]
					// The correct label is "quantile" but to not break old expressions
					// this remains "percentile"
					hist.Labels["percentile"] = model.LabelValue(q)

					f.Metric = append(f.Metric, &dto.Metric{
						Label: protoLabelSet(e.BaseLabels, hist.Labels),
						Untyped: &dto.Untyped{
							Value: proto.Float64(value),
						},
					})
				}
			}

		default:
			return fmt.Errorf("unknown metric type %q", e.Metric.Type)
		}
	}
	return nil
}
Пример #5
0
// readingValue represents the state where the last byte read (now in
// p.currentByte) is the first byte of the sample value (i.e. a float).
func (p *TextParser) readingValue() stateFn {
	// When we are here, we have read all the labels, so for the
	// special case of a summary/histogram, we can finally find out
	// if the metric already exists.
	if p.currentMF.GetType() == dto.MetricType_SUMMARY {
		signature := model.LabelsToSignature(p.currentLabels)
		if summary := p.summaries[signature]; summary != nil {
			p.currentMetric = summary
		} else {
			p.summaries[signature] = p.currentMetric
			p.currentMF.Metric = append(p.currentMF.Metric, p.currentMetric)
		}
	} else if p.currentMF.GetType() == dto.MetricType_HISTOGRAM {
		signature := model.LabelsToSignature(p.currentLabels)
		if histogram := p.histograms[signature]; histogram != nil {
			p.currentMetric = histogram
		} else {
			p.histograms[signature] = p.currentMetric
			p.currentMF.Metric = append(p.currentMF.Metric, p.currentMetric)
		}
	} else {
		p.currentMF.Metric = append(p.currentMF.Metric, p.currentMetric)
	}
	if p.readTokenUntilWhitespace(); p.err != nil {
		return nil // Unexpected end of input.
	}
	value, err := strconv.ParseFloat(p.currentToken.String(), 64)
	if err != nil {
		// Create a more helpful error message.
		p.parseError(fmt.Sprintf("expected float as value, got %q", p.currentToken.String()))
		return nil
	}
	switch p.currentMF.GetType() {
	case dto.MetricType_COUNTER:
		p.currentMetric.Counter = &dto.Counter{Value: proto.Float64(value)}
	case dto.MetricType_GAUGE:
		p.currentMetric.Gauge = &dto.Gauge{Value: proto.Float64(value)}
	case dto.MetricType_UNTYPED:
		p.currentMetric.Untyped = &dto.Untyped{Value: proto.Float64(value)}
	case dto.MetricType_SUMMARY:
		// *sigh*
		if p.currentMetric.Summary == nil {
			p.currentMetric.Summary = &dto.Summary{}
		}
		switch {
		case p.currentIsSummaryCount:
			p.currentMetric.Summary.SampleCount = proto.Uint64(uint64(value))
		case p.currentIsSummarySum:
			p.currentMetric.Summary.SampleSum = proto.Float64(value)
		case !math.IsNaN(p.currentQuantile):
			p.currentMetric.Summary.Quantile = append(
				p.currentMetric.Summary.Quantile,
				&dto.Quantile{
					Quantile: proto.Float64(p.currentQuantile),
					Value:    proto.Float64(value),
				},
			)
		}
	case dto.MetricType_HISTOGRAM:
		// *sigh*
		if p.currentMetric.Histogram == nil {
			p.currentMetric.Histogram = &dto.Histogram{}
		}
		switch {
		case p.currentIsHistogramCount:
			p.currentMetric.Histogram.SampleCount = proto.Uint64(uint64(value))
		case p.currentIsHistogramSum:
			p.currentMetric.Histogram.SampleSum = proto.Float64(value)
		case !math.IsNaN(p.currentBucket):
			p.currentMetric.Histogram.Bucket = append(
				p.currentMetric.Histogram.Bucket,
				&dto.Bucket{
					UpperBound:      proto.Float64(p.currentBucket),
					CumulativeCount: proto.Uint64(uint64(value)),
				},
			)
		}
	default:
		p.err = fmt.Errorf("unexpected type for metric name %q", p.currentMF.GetName())
	}
	if p.currentByte == '\n' {
		return p.startOfLine
	}
	return p.startTimestamp
}