Source: decode.go in package github.com/prometheus/common/expfmt

Copyright 2015 The Prometheus Authors Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


package expfmt

import (
	"fmt"
	"io"
	"math"
	"mime"
	"net/http"

	dto "github.com/prometheus/client_model/go"

	"github.com/matttproud/golang_protobuf_extensions/pbutil"
	"github.com/prometheus/common/model"
)

Decoder types decode an input stream into metric families.

type Decoder interface {
	Decode(*dto.MetricFamily) error
}

DecodeOptions contains options used by the Decoder and in sample extraction.

Timestamp is added to each value from the stream that has no explicit timestamp set.

	Timestamp model.Time
}

ResponseFormat extracts the correct format from a HTTP response header. If no matching format can be found FormatUnknown is returned.

func ResponseFormat(h http.Header) Format {
	ct := h.Get(hdrContentType)

	mediatype, params, err := mime.ParseMediaType(ct)
	if err != nil {
		return FmtUnknown
	}

	const textType = "text/plain"

	switch mediatype {
	case ProtoType:
		if p, ok := params["proto"]; ok && p != ProtoProtocol {
			return FmtUnknown
		}
		if e, ok := params["encoding"]; ok && e != "delimited" {
			return FmtUnknown
		}
		return FmtProtoDelim

	case textType:
		if v, ok := params["version"]; ok && v != TextVersion {
			return FmtUnknown
		}
		return FmtText
	}

	return FmtUnknown
}

NewDecoder returns a new decoder based on the given input format. If the input format does not imply otherwise, a text format decoder is returned.

func NewDecoder(r io.Reader, format Format) Decoder {
	switch format {
	case FmtProtoDelim:
		return &protoDecoder{r: r}
	}
	return &textDecoder{r: r}
}

protoDecoder implements the Decoder interface for protocol buffers.

type protoDecoder struct {
	r io.Reader
}

Decode implements the Decoder interface.

func (d *protoDecoder) Decode(v *dto.MetricFamily) error {
	_, err := pbutil.ReadDelimited(d.r, v)
	if err != nil {
		return err
	}
	if !model.IsValidMetricName(model.LabelValue(v.GetName())) {
		return fmt.Errorf("invalid metric name %q", v.GetName())
	}
	for _, m := range v.GetMetric() {
		if m == nil {
			continue
		}
		for _, l := range m.GetLabel() {
			if l == nil {
				continue
			}
			if !model.LabelValue(l.GetValue()).IsValid() {
				return fmt.Errorf("invalid label value %q", l.GetValue())
			}
			if !model.LabelName(l.GetName()).IsValid() {
				return fmt.Errorf("invalid label name %q", l.GetName())
			}
		}
	}
	return nil
}

textDecoder implements the Decoder interface for the text protocol.

type textDecoder struct {
	r    io.Reader
	p    TextParser
	fams []*dto.MetricFamily
}

Decode implements the Decoder interface.

TODO(fabxc): Wrap this as a line reader to make streaming safer.

No cached metric families, read everything and parse metrics.

		fams, err := d.p.TextToMetricFamilies(d.r)
		if err != nil {
			return err
		}
		if len(fams) == 0 {
			return io.EOF
		}
		d.fams = make([]*dto.MetricFamily, 0, len(fams))
		for _, f := range fams {
			d.fams = append(d.fams, f)
		}
	}

	*v = *d.fams[0]
	d.fams = d.fams[1:]

	return nil
}

SampleDecoder wraps a Decoder to extract samples from the metric families decoded by the wrapped Decoder.

type SampleDecoder struct {
	Dec  Decoder
	Opts *DecodeOptions

	f dto.MetricFamily
}

Decode calls the Decode method of the wrapped Decoder and then extracts the samples from the decoded MetricFamily into the provided model.Vector.

func (sd *SampleDecoder) Decode(s *model.Vector) error {
	err := sd.Dec.Decode(&sd.f)
	if err != nil {
		return err
	}
	*s, err = extractSamples(&sd.f, sd.Opts)
	return err
}

ExtractSamples builds a slice of samples from the provided metric families. If an error occurrs during sample extraction, it continues to extract from the remaining metric families. The returned error is the last error that has occurred.

func ExtractSamples(o *DecodeOptions, fams ...*dto.MetricFamily) (model.Vector, error) {
	var (
		all     model.Vector
		lastErr error
	)
	for _, f := range fams {
		some, err := extractSamples(f, o)
		if err != nil {
			lastErr = err
			continue
		}
		all = append(all, some...)
	}
	return all, lastErr
}

func extractSamples(f *dto.MetricFamily, o *DecodeOptions) (model.Vector, error) {
	switch f.GetType() {
	case dto.MetricType_COUNTER:
		return extractCounter(o, f), nil
	case dto.MetricType_GAUGE:
		return extractGauge(o, f), nil
	case dto.MetricType_SUMMARY:
		return extractSummary(o, f), nil
	case dto.MetricType_UNTYPED:
		return extractUntyped(o, f), nil
	case dto.MetricType_HISTOGRAM:
		return extractHistogram(o, f), nil
	}
	return nil, fmt.Errorf("expfmt.extractSamples: unknown metric family type %v", f.GetType())
}

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

	for _, m := range f.Metric {
		if m.Counter == 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.Counter.GetValue()),
		}

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

		samples = append(samples, smpl)
	}

	return samples
}

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

	for _, m := range f.Metric {
		if m.Gauge == 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.Gauge.GetValue()),
		}

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

		samples = append(samples, smpl)
	}

	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 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 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)

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