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 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 protoLabelSet(base, ext model.LabelSet) []*dto.LabelPair {
labels := base.Clone().Merge(ext)
delete(labels, model.MetricNameLabel)
names := make([]string, 0, len(labels))
for ln := range labels {
names = append(names, string(ln))
}
sort.Strings(names)
pairs := make([]*dto.LabelPair, 0, len(labels))
for _, ln := range names {
lv := labels[model.LabelName(ln)]
pairs = append(pairs, &dto.LabelPair{
Name: proto.String(ln),
Value: proto.String(string(lv)),
})
}
return pairs
}
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
}