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 (r *registry) checkConsistency(metricFamily *dto.MetricFamily, dtoMetric *dto.Metric, desc *Desc, metricHashes map[uint64]struct{}) error {
// Type consistency with metric family.
if metricFamily.GetType() == dto.MetricType_GAUGE && dtoMetric.Gauge == nil ||
metricFamily.GetType() == dto.MetricType_COUNTER && dtoMetric.Counter == nil ||
metricFamily.GetType() == dto.MetricType_SUMMARY && dtoMetric.Summary == nil ||
metricFamily.GetType() == dto.MetricType_HISTOGRAM && dtoMetric.Histogram == nil ||
metricFamily.GetType() == dto.MetricType_UNTYPED && dtoMetric.Untyped == nil {
return fmt.Errorf(
"collected metric %s %s is not a %s",
metricFamily.GetName(), dtoMetric, metricFamily.GetType(),
)
}
// Is the metric unique (i.e. no other metric with the same name and the same label values)?
h := hashNew()
h = hashAdd(h, metricFamily.GetName())
h = hashAddByte(h, separatorByte)
// Make sure label pairs are sorted. We depend on it for the consistency
// check. Label pairs must be sorted by contract. But the point of this
// method is to check for contract violations. So we better do the sort
// now.
sort.Sort(LabelPairSorter(dtoMetric.Label))
for _, lp := range dtoMetric.Label {
h = hashAdd(h, lp.GetValue())
h = hashAddByte(h, separatorByte)
}
if _, exists := metricHashes[h]; exists {
return fmt.Errorf(
"collected metric %s %s was collected before with the same name and label values",
metricFamily.GetName(), dtoMetric,
)
}
metricHashes[h] = struct{}{}
if desc == nil {
return nil // Nothing left to check if we have no desc.
}
// Desc consistency with metric family.
if metricFamily.GetName() != desc.fqName {
return fmt.Errorf(
"collected metric %s %s has name %q but should have %q",
metricFamily.GetName(), dtoMetric, metricFamily.GetName(), desc.fqName,
)
}
if metricFamily.GetHelp() != desc.help {
return fmt.Errorf(
"collected metric %s %s has help %q but should have %q",
metricFamily.GetName(), dtoMetric, metricFamily.GetHelp(), desc.help,
)
}
// Is the desc consistent with the content of the metric?
lpsFromDesc := make([]*dto.LabelPair, 0, len(dtoMetric.Label))
lpsFromDesc = append(lpsFromDesc, desc.constLabelPairs...)
for _, l := range desc.variableLabels {
lpsFromDesc = append(lpsFromDesc, &dto.LabelPair{
Name: proto.String(l),
})
}
if len(lpsFromDesc) != len(dtoMetric.Label) {
return fmt.Errorf(
"labels in collected metric %s %s are inconsistent with descriptor %s",
metricFamily.GetName(), dtoMetric, desc,
)
}
sort.Sort(LabelPairSorter(lpsFromDesc))
for i, lpFromDesc := range lpsFromDesc {
lpFromMetric := dtoMetric.Label[i]
if lpFromDesc.GetName() != lpFromMetric.GetName() ||
lpFromDesc.Value != nil && lpFromDesc.GetValue() != lpFromMetric.GetValue() {
return fmt.Errorf(
"labels in collected metric %s %s are inconsistent with descriptor %s",
metricFamily.GetName(), dtoMetric, desc,
)
}
}
r.mtx.RLock() // Remaining checks need the read lock.
defer r.mtx.RUnlock()
// Is the desc registered?
if _, exist := r.descIDs[desc.id]; !exist {
return fmt.Errorf(
"collected metric %s %s with unregistered descriptor %s",
metricFamily.GetName(), dtoMetric, desc,
)
}
return nil
}
// MetricFamilyToText converts a MetricFamily proto message into text format and
// writes the resulting lines to 'out'. It returns the number of bytes written
// and any error encountered. This function does not perform checks on the
// content of the metric and label names, i.e. invalid metric or label names
// will result in invalid text format output.
// This method fulfills the type 'prometheus.encoder'.
func MetricFamilyToText(out io.Writer, in *dto.MetricFamily) (int, error) {
var written int
// Fail-fast checks.
if len(in.Metric) == 0 {
return written, fmt.Errorf("MetricFamily has no metrics: %s", in)
}
name := in.GetName()
if name == "" {
return written, fmt.Errorf("MetricFamily has no name: %s", in)
}
// Comments, first HELP, then TYPE.
if in.Help != nil {
n, err := fmt.Fprintf(
out, "# HELP %s %s\n",
name, escapeString(*in.Help, false),
)
written += n
if err != nil {
return written, err
}
}
metricType := in.GetType()
n, err := fmt.Fprintf(
out, "# TYPE %s %s\n",
name, strings.ToLower(metricType.String()),
)
written += n
if err != nil {
return written, err
}
// Finally the samples, one line for each.
for _, metric := range in.Metric {
switch metricType {
case dto.MetricType_COUNTER:
if metric.Counter == nil {
return written, fmt.Errorf(
"expected counter in metric %s %s", name, metric,
)
}
n, err = writeSample(
name, metric, "", "",
metric.Counter.GetValue(),
out,
)
case dto.MetricType_GAUGE:
if metric.Gauge == nil {
return written, fmt.Errorf(
"expected gauge in metric %s %s", name, metric,
)
}
n, err = writeSample(
name, metric, "", "",
metric.Gauge.GetValue(),
out,
)
case dto.MetricType_UNTYPED:
if metric.Untyped == nil {
return written, fmt.Errorf(
"expected untyped in metric %s %s", name, metric,
)
}
n, err = writeSample(
name, metric, "", "",
metric.Untyped.GetValue(),
out,
)
case dto.MetricType_SUMMARY:
if metric.Summary == nil {
return written, fmt.Errorf(
"expected summary in metric %s %s", name, metric,
)
}
for _, q := range metric.Summary.Quantile {
n, err = writeSample(
name, metric,
model.QuantileLabel, fmt.Sprint(q.GetQuantile()),
q.GetValue(),
out,
)
written += n
if err != nil {
return written, err
}
}
n, err = writeSample(
name+"_sum", metric, "", "",
metric.Summary.GetSampleSum(),
out,
)
if err != nil {
return written, err
}
written += n
n, err = writeSample(
name+"_count", metric, "", "",
float64(metric.Summary.GetSampleCount()),
out,
)
case dto.MetricType_HISTOGRAM:
if metric.Histogram == nil {
return written, fmt.Errorf(
"expected histogram in metric %s %s", name, metric,
)
}
infSeen := false
for _, q := range metric.Histogram.Bucket {
n, err = writeSample(
name+"_bucket", metric,
model.BucketLabel, fmt.Sprint(q.GetUpperBound()),
float64(q.GetCumulativeCount()),
out,
)
written += n
if err != nil {
return written, err
}
if math.IsInf(q.GetUpperBound(), +1) {
infSeen = true
}
}
if !infSeen {
n, err = writeSample(
name+"_bucket", metric,
model.BucketLabel, "+Inf",
float64(metric.Histogram.GetSampleCount()),
out,
)
if err != nil {
return written, err
}
written += n
}
n, err = writeSample(
name+"_sum", metric, "", "",
metric.Histogram.GetSampleSum(),
out,
)
if err != nil {
return written, err
}
written += n
n, err = writeSample(
name+"_count", metric, "", "",
float64(metric.Histogram.GetSampleCount()),
out,
)
default:
return written, fmt.Errorf(
"unexpected type in metric %s %s", name, metric,
)
}
written += n
if err != nil {
return written, err
}
}
return written, 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
}