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
}
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
}
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
}
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
}
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
}
// 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
}
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
}