예제 #1
0
func extractUntyped(o *DecodeOptions, f *dto.MetricFamily) model.Vector {
	samples := make(model.Vector, 0, len(f.Metric))

	for _, m := range f.Metric {
		if m.Untyped == nil {
			continue
		}

		lset := make(model.LabelSet, len(m.Label)+1)
		for _, p := range m.Label {
			lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
		}
		lset[model.MetricNameLabel] = model.LabelValue(f.GetName())

		smpl := &model.Sample{
			Metric: model.Metric(lset),
			Value:  model.SampleValue(m.Untyped.GetValue()),
		}

		if m.TimestampMs != nil {
			smpl.Timestamp = model.TimeFromUnixNano(*m.TimestampMs * 1000000)
		} else {
			smpl.Timestamp = o.Timestamp
		}

		samples = append(samples, smpl)
	}

	return samples
}
예제 #2
0
func extractSummary(o *DecodeOptions, f *dto.MetricFamily) model.Vector {
	samples := make(model.Vector, 0, len(f.Metric))

	for _, m := range f.Metric {
		if m.Summary == nil {
			continue
		}

		timestamp := o.Timestamp
		if m.TimestampMs != nil {
			timestamp = model.TimeFromUnixNano(*m.TimestampMs * 1000000)
		}

		for _, q := range m.Summary.Quantile {
			lset := make(model.LabelSet, len(m.Label)+2)
			for _, p := range m.Label {
				lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
			}
			// BUG(matt): Update other names to "quantile".
			lset[model.LabelName(model.QuantileLabel)] = model.LabelValue(fmt.Sprint(q.GetQuantile()))
			lset[model.MetricNameLabel] = model.LabelValue(f.GetName())

			samples = append(samples, &model.Sample{
				Metric:    model.Metric(lset),
				Value:     model.SampleValue(q.GetValue()),
				Timestamp: timestamp,
			})
		}

		lset := make(model.LabelSet, len(m.Label)+1)
		for _, p := range m.Label {
			lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
		}
		lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_sum")

		samples = append(samples, &model.Sample{
			Metric:    model.Metric(lset),
			Value:     model.SampleValue(m.Summary.GetSampleSum()),
			Timestamp: timestamp,
		})

		lset = make(model.LabelSet, len(m.Label)+1)
		for _, p := range m.Label {
			lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
		}
		lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_count")

		samples = append(samples, &model.Sample{
			Metric:    model.Metric(lset),
			Value:     model.SampleValue(m.Summary.GetSampleCount()),
			Timestamp: timestamp,
		})
	}

	return samples
}
예제 #3
0
func extractHistogram(o *DecodeOptions, f *dto.MetricFamily) model.Vector {
	samples := make(model.Vector, 0, len(f.Metric))

	for _, m := range f.Metric {
		if m.Histogram == nil {
			continue
		}

		timestamp := o.Timestamp
		if m.TimestampMs != nil {
			timestamp = model.TimeFromUnixNano(*m.TimestampMs * 1000000)
		}

		infSeen := false

		for _, q := range m.Histogram.Bucket {
			lset := make(model.LabelSet, len(m.Label)+2)
			for _, p := range m.Label {
				lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
			}
			lset[model.LabelName(model.BucketLabel)] = model.LabelValue(fmt.Sprint(q.GetUpperBound()))
			lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_bucket")

			if math.IsInf(q.GetUpperBound(), +1) {
				infSeen = true
			}

			samples = append(samples, &model.Sample{
				Metric:    model.Metric(lset),
				Value:     model.SampleValue(q.GetCumulativeCount()),
				Timestamp: timestamp,
			})
		}

		lset := make(model.LabelSet, len(m.Label)+1)
		for _, p := range m.Label {
			lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
		}
		lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_sum")

		samples = append(samples, &model.Sample{
			Metric:    model.Metric(lset),
			Value:     model.SampleValue(m.Histogram.GetSampleSum()),
			Timestamp: timestamp,
		})

		lset = make(model.LabelSet, len(m.Label)+1)
		for _, p := range m.Label {
			lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
		}
		lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_count")

		count := &model.Sample{
			Metric:    model.Metric(lset),
			Value:     model.SampleValue(m.Histogram.GetSampleCount()),
			Timestamp: timestamp,
		}
		samples = append(samples, count)

		if !infSeen {
			// Append an infinity bucket sample.
			lset := make(model.LabelSet, len(m.Label)+2)
			for _, p := range m.Label {
				lset[model.LabelName(p.GetName())] = model.LabelValue(p.GetValue())
			}
			lset[model.LabelName(model.BucketLabel)] = model.LabelValue("+Inf")
			lset[model.MetricNameLabel] = model.LabelValue(f.GetName() + "_bucket")

			samples = append(samples, &model.Sample{
				Metric:    model.Metric(lset),
				Value:     count.Value,
				Timestamp: timestamp,
			})
		}
	}

	return samples
}
예제 #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
}