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
}
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
}
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
}
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
}