prometheus/model/textparse/protobufparse.go
beorn7 9e500345f3 textparse/scrape: Add option to scrape both classic and native histograms
So far, if a target exposes a histogram with both classic and native
buckets, a native-histogram enabled Prometheus would ignore the
classic buckets. With the new scrape config option
`scrape_classic_histograms` set, both buckets will be ingested,
creating all the series of a classic histogram in parallel to the
native histogram series. For example, a histogram `foo` would create a
native histogram series `foo` and classic series called `foo_sum`,
`foo_count`, and `foo_bucket`.

This feature can be used in a migration strategy from classic to
native histograms, where it is desired to have a transition period
during which both native and classic histograms are present.

Note that two bugs in classic histogram parsing were found and fixed
as a byproduct of testing the new feature:

1. Series created from classic _gauge_ histograms didn't get the
   _sum/_count/_bucket prefix set.
2. Values of classic _float_ histograms weren't parsed properly.

Signed-off-by: beorn7 <beorn@grafana.com>
2023-05-13 01:32:25 +02:00

564 lines
18 KiB
Go

// Copyright 2021 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 textparse
import (
"bytes"
"encoding/binary"
"fmt"
"io"
"math"
"strings"
"unicode/utf8"
"github.com/gogo/protobuf/proto"
"github.com/pkg/errors"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/model/exemplar"
"github.com/prometheus/prometheus/model/histogram"
"github.com/prometheus/prometheus/model/labels"
dto "github.com/prometheus/prometheus/prompb/io/prometheus/client"
)
// ProtobufParser is a very inefficient way of unmarshaling the old Prometheus
// protobuf format and then present it as it if were parsed by a
// Prometheus-2-style text parser. This is only done so that we can easily plug
// in the protobuf format into Prometheus 2. For future use (with the final
// format that will be used for native histograms), we have to revisit the
// parsing. A lot of the efficiency tricks of the Prometheus-2-style parsing
// could be used in a similar fashion (byte-slice pointers into the raw
// payload), which requires some hand-coded protobuf handling. But the current
// parsers all expect the full series name (metric name plus label pairs) as one
// string, which is not how things are represented in the protobuf format. If
// the re-arrangement work is actually causing problems (which has to be seen),
// that expectation needs to be changed.
type ProtobufParser struct {
in []byte // The intput to parse.
inPos int // Position within the input.
metricPos int // Position within Metric slice.
// fieldPos is the position within a Summary or (legacy) Histogram. -2
// is the count. -1 is the sum. Otherwise it is the index within
// quantiles/buckets.
fieldPos int
fieldsDone bool // true if no more fields of a Summary or (legacy) Histogram to be processed.
redoClassic bool // true after parsing a native histogram if we need to parse it again as a classit histogram.
// state is marked by the entry we are processing. EntryInvalid implies
// that we have to decode the next MetricFamily.
state Entry
builder labels.ScratchBuilder // held here to reduce allocations when building Labels
mf *dto.MetricFamily
// Wether to also parse a classic histogram that is also present as a
// native histogram.
parseClassicHistograms bool
// The following are just shenanigans to satisfy the Parser interface.
metricBytes *bytes.Buffer // A somewhat fluid representation of the current metric.
}
// NewProtobufParser returns a parser for the payload in the byte slice.
func NewProtobufParser(b []byte, parseClassicHistograms bool) Parser {
return &ProtobufParser{
in: b,
state: EntryInvalid,
mf: &dto.MetricFamily{},
metricBytes: &bytes.Buffer{},
parseClassicHistograms: parseClassicHistograms,
}
}
// Series returns the bytes of a series with a simple float64 as a
// value, the timestamp if set, and the value of the current sample.
func (p *ProtobufParser) Series() ([]byte, *int64, float64) {
var (
m = p.mf.GetMetric()[p.metricPos]
ts = m.GetTimestampMs()
v float64
)
switch p.mf.GetType() {
case dto.MetricType_COUNTER:
v = m.GetCounter().GetValue()
case dto.MetricType_GAUGE:
v = m.GetGauge().GetValue()
case dto.MetricType_UNTYPED:
v = m.GetUntyped().GetValue()
case dto.MetricType_SUMMARY:
s := m.GetSummary()
switch p.fieldPos {
case -2:
v = float64(s.GetSampleCount())
case -1:
v = s.GetSampleSum()
// Need to detect a summaries without quantile here.
if len(s.GetQuantile()) == 0 {
p.fieldsDone = true
}
default:
v = s.GetQuantile()[p.fieldPos].GetValue()
}
case dto.MetricType_HISTOGRAM, dto.MetricType_GAUGE_HISTOGRAM:
// This should only happen for a classic histogram.
h := m.GetHistogram()
switch p.fieldPos {
case -2:
v = h.GetSampleCountFloat()
if v == 0 {
v = float64(h.GetSampleCount())
}
case -1:
v = h.GetSampleSum()
default:
bb := h.GetBucket()
if p.fieldPos >= len(bb) {
v = h.GetSampleCountFloat()
if v == 0 {
v = float64(h.GetSampleCount())
}
} else {
v = bb[p.fieldPos].GetCumulativeCountFloat()
if v == 0 {
v = float64(bb[p.fieldPos].GetCumulativeCount())
}
}
}
default:
panic("encountered unexpected metric type, this is a bug")
}
if ts != 0 {
return p.metricBytes.Bytes(), &ts, v
}
// Nasty hack: Assume that ts==0 means no timestamp. That's not true in
// general, but proto3 has no distinction between unset and
// default. Need to avoid in the final format.
return p.metricBytes.Bytes(), nil, v
}
// Histogram returns the bytes of a series with a native histogram as a value,
// the timestamp if set, and the native histogram in the current sample.
//
// The Compact method is called before returning the Histogram (or FloatHistogram).
//
// If the SampleCountFloat or the ZeroCountFloat in the proto message is > 0,
// the histogram is parsed and returned as a FloatHistogram and nil is returned
// as the (integer) Histogram return value. Otherwise, it is parsed and returned
// as an (integer) Histogram and nil is returned as the FloatHistogram return
// value.
func (p *ProtobufParser) Histogram() ([]byte, *int64, *histogram.Histogram, *histogram.FloatHistogram) {
var (
m = p.mf.GetMetric()[p.metricPos]
ts = m.GetTimestampMs()
h = m.GetHistogram()
)
if p.parseClassicHistograms && len(h.GetBucket()) > 0 {
p.redoClassic = true
}
if h.GetSampleCountFloat() > 0 || h.GetZeroCountFloat() > 0 {
// It is a float histogram.
fh := histogram.FloatHistogram{
Count: h.GetSampleCountFloat(),
Sum: h.GetSampleSum(),
ZeroThreshold: h.GetZeroThreshold(),
ZeroCount: h.GetZeroCountFloat(),
Schema: h.GetSchema(),
PositiveSpans: make([]histogram.Span, len(h.GetPositiveSpan())),
PositiveBuckets: h.GetPositiveCount(),
NegativeSpans: make([]histogram.Span, len(h.GetNegativeSpan())),
NegativeBuckets: h.GetNegativeCount(),
}
for i, span := range h.GetPositiveSpan() {
fh.PositiveSpans[i].Offset = span.GetOffset()
fh.PositiveSpans[i].Length = span.GetLength()
}
for i, span := range h.GetNegativeSpan() {
fh.NegativeSpans[i].Offset = span.GetOffset()
fh.NegativeSpans[i].Length = span.GetLength()
}
if p.mf.GetType() == dto.MetricType_GAUGE_HISTOGRAM {
fh.CounterResetHint = histogram.GaugeType
}
fh.Compact(0)
if ts != 0 {
return p.metricBytes.Bytes(), &ts, nil, &fh
}
// Nasty hack: Assume that ts==0 means no timestamp. That's not true in
// general, but proto3 has no distinction between unset and
// default. Need to avoid in the final format.
return p.metricBytes.Bytes(), nil, nil, &fh
}
sh := histogram.Histogram{
Count: h.GetSampleCount(),
Sum: h.GetSampleSum(),
ZeroThreshold: h.GetZeroThreshold(),
ZeroCount: h.GetZeroCount(),
Schema: h.GetSchema(),
PositiveSpans: make([]histogram.Span, len(h.GetPositiveSpan())),
PositiveBuckets: h.GetPositiveDelta(),
NegativeSpans: make([]histogram.Span, len(h.GetNegativeSpan())),
NegativeBuckets: h.GetNegativeDelta(),
}
for i, span := range h.GetPositiveSpan() {
sh.PositiveSpans[i].Offset = span.GetOffset()
sh.PositiveSpans[i].Length = span.GetLength()
}
for i, span := range h.GetNegativeSpan() {
sh.NegativeSpans[i].Offset = span.GetOffset()
sh.NegativeSpans[i].Length = span.GetLength()
}
if p.mf.GetType() == dto.MetricType_GAUGE_HISTOGRAM {
sh.CounterResetHint = histogram.GaugeType
}
sh.Compact(0)
if ts != 0 {
return p.metricBytes.Bytes(), &ts, &sh, nil
}
return p.metricBytes.Bytes(), nil, &sh, nil
}
// Help returns the metric name and help text in the current entry.
// Must only be called after Next returned a help entry.
// The returned byte slices become invalid after the next call to Next.
func (p *ProtobufParser) Help() ([]byte, []byte) {
return p.metricBytes.Bytes(), []byte(p.mf.GetHelp())
}
// Type returns the metric name and type in the current entry.
// Must only be called after Next returned a type entry.
// The returned byte slices become invalid after the next call to Next.
func (p *ProtobufParser) Type() ([]byte, MetricType) {
n := p.metricBytes.Bytes()
switch p.mf.GetType() {
case dto.MetricType_COUNTER:
return n, MetricTypeCounter
case dto.MetricType_GAUGE:
return n, MetricTypeGauge
case dto.MetricType_HISTOGRAM:
return n, MetricTypeHistogram
case dto.MetricType_GAUGE_HISTOGRAM:
return n, MetricTypeGaugeHistogram
case dto.MetricType_SUMMARY:
return n, MetricTypeSummary
}
return n, MetricTypeUnknown
}
// Unit always returns (nil, nil) because units aren't supported by the protobuf
// format.
func (p *ProtobufParser) Unit() ([]byte, []byte) {
return nil, nil
}
// Comment always returns nil because comments aren't supported by the protobuf
// format.
func (p *ProtobufParser) Comment() []byte {
return nil
}
// Metric writes the labels of the current sample into the passed labels.
// It returns the string from which the metric was parsed.
func (p *ProtobufParser) Metric(l *labels.Labels) string {
p.builder.Reset()
p.builder.Add(labels.MetricName, p.getMagicName())
for _, lp := range p.mf.GetMetric()[p.metricPos].GetLabel() {
p.builder.Add(lp.GetName(), lp.GetValue())
}
if needed, name, value := p.getMagicLabel(); needed {
p.builder.Add(name, value)
}
// Sort labels to maintain the sorted labels invariant.
p.builder.Sort()
*l = p.builder.Labels()
return p.metricBytes.String()
}
// Exemplar writes the exemplar of the current sample into the passed
// exemplar. It returns if an exemplar exists or not. In case of a native
// histogram, the legacy bucket section is still used for exemplars. To ingest
// all examplars, call the Exemplar method repeatedly until it returns false.
func (p *ProtobufParser) Exemplar(ex *exemplar.Exemplar) bool {
m := p.mf.GetMetric()[p.metricPos]
var exProto *dto.Exemplar
switch p.mf.GetType() {
case dto.MetricType_COUNTER:
exProto = m.GetCounter().GetExemplar()
case dto.MetricType_HISTOGRAM, dto.MetricType_GAUGE_HISTOGRAM:
bb := m.GetHistogram().GetBucket()
if p.fieldPos < 0 {
if p.state == EntrySeries {
return false // At _count or _sum.
}
p.fieldPos = 0 // Start at 1st bucket for native histograms.
}
for p.fieldPos < len(bb) {
exProto = bb[p.fieldPos].GetExemplar()
if p.state == EntrySeries {
break
}
p.fieldPos++
if exProto != nil {
break
}
}
default:
return false
}
if exProto == nil {
return false
}
ex.Value = exProto.GetValue()
if ts := exProto.GetTimestamp(); ts != nil {
ex.HasTs = true
ex.Ts = ts.GetSeconds()*1000 + int64(ts.GetNanos()/1_000_000)
}
p.builder.Reset()
for _, lp := range exProto.GetLabel() {
p.builder.Add(lp.GetName(), lp.GetValue())
}
p.builder.Sort()
ex.Labels = p.builder.Labels()
return true
}
// Next advances the parser to the next "sample" (emulating the behavior of a
// text format parser). It returns (EntryInvalid, io.EOF) if no samples were
// read.
func (p *ProtobufParser) Next() (Entry, error) {
switch p.state {
case EntryInvalid:
p.metricPos = 0
p.fieldPos = -2
n, err := readDelimited(p.in[p.inPos:], p.mf)
p.inPos += n
if err != nil {
return p.state, err
}
// Skip empty metric families.
if len(p.mf.GetMetric()) == 0 {
return p.Next()
}
// We are at the beginning of a metric family. Put only the name
// into metricBytes and validate only name, help, and type for now.
name := p.mf.GetName()
if !model.IsValidMetricName(model.LabelValue(name)) {
return EntryInvalid, errors.Errorf("invalid metric name: %s", name)
}
if help := p.mf.GetHelp(); !utf8.ValidString(help) {
return EntryInvalid, errors.Errorf("invalid help for metric %q: %s", name, help)
}
switch p.mf.GetType() {
case dto.MetricType_COUNTER,
dto.MetricType_GAUGE,
dto.MetricType_HISTOGRAM,
dto.MetricType_GAUGE_HISTOGRAM,
dto.MetricType_SUMMARY,
dto.MetricType_UNTYPED:
// All good.
default:
return EntryInvalid, errors.Errorf("unknown metric type for metric %q: %s", name, p.mf.GetType())
}
p.metricBytes.Reset()
p.metricBytes.WriteString(name)
p.state = EntryHelp
case EntryHelp:
p.state = EntryType
case EntryType:
t := p.mf.GetType()
if (t == dto.MetricType_HISTOGRAM || t == dto.MetricType_GAUGE_HISTOGRAM) &&
isNativeHistogram(p.mf.GetMetric()[0].GetHistogram()) {
p.state = EntryHistogram
} else {
p.state = EntrySeries
}
if err := p.updateMetricBytes(); err != nil {
return EntryInvalid, err
}
case EntryHistogram, EntrySeries:
if p.redoClassic {
p.redoClassic = false
p.state = EntrySeries
p.fieldPos = -3
p.fieldsDone = false
}
t := p.mf.GetType()
if p.state == EntrySeries && !p.fieldsDone &&
(t == dto.MetricType_SUMMARY ||
t == dto.MetricType_HISTOGRAM ||
t == dto.MetricType_GAUGE_HISTOGRAM) {
p.fieldPos++
} else {
p.metricPos++
p.fieldPos = -2
p.fieldsDone = false
}
if p.metricPos >= len(p.mf.GetMetric()) {
p.state = EntryInvalid
return p.Next()
}
if err := p.updateMetricBytes(); err != nil {
return EntryInvalid, err
}
default:
return EntryInvalid, errors.Errorf("invalid protobuf parsing state: %d", p.state)
}
return p.state, nil
}
func (p *ProtobufParser) updateMetricBytes() error {
b := p.metricBytes
b.Reset()
b.WriteString(p.getMagicName())
for _, lp := range p.mf.GetMetric()[p.metricPos].GetLabel() {
b.WriteByte(model.SeparatorByte)
n := lp.GetName()
if !model.LabelName(n).IsValid() {
return errors.Errorf("invalid label name: %s", n)
}
b.WriteString(n)
b.WriteByte(model.SeparatorByte)
v := lp.GetValue()
if !utf8.ValidString(v) {
return errors.Errorf("invalid label value: %s", v)
}
b.WriteString(v)
}
if needed, n, v := p.getMagicLabel(); needed {
b.WriteByte(model.SeparatorByte)
b.WriteString(n)
b.WriteByte(model.SeparatorByte)
b.WriteString(v)
}
return nil
}
// getMagicName usually just returns p.mf.GetType() but adds a magic suffix
// ("_count", "_sum", "_bucket") if needed according to the current parser
// state.
func (p *ProtobufParser) getMagicName() string {
t := p.mf.GetType()
if p.state == EntryHistogram || (t != dto.MetricType_HISTOGRAM && t != dto.MetricType_GAUGE_HISTOGRAM && t != dto.MetricType_SUMMARY) {
return p.mf.GetName()
}
if p.fieldPos == -2 {
return p.mf.GetName() + "_count"
}
if p.fieldPos == -1 {
return p.mf.GetName() + "_sum"
}
if t == dto.MetricType_HISTOGRAM || t == dto.MetricType_GAUGE_HISTOGRAM {
return p.mf.GetName() + "_bucket"
}
return p.mf.GetName()
}
// getMagicLabel returns if a magic label ("quantile" or "le") is needed and, if
// so, its name and value. It also sets p.fieldsDone if applicable.
func (p *ProtobufParser) getMagicLabel() (bool, string, string) {
if p.state == EntryHistogram || p.fieldPos < 0 {
return false, "", ""
}
switch p.mf.GetType() {
case dto.MetricType_SUMMARY:
qq := p.mf.GetMetric()[p.metricPos].GetSummary().GetQuantile()
q := qq[p.fieldPos]
p.fieldsDone = p.fieldPos == len(qq)-1
return true, model.QuantileLabel, formatOpenMetricsFloat(q.GetQuantile())
case dto.MetricType_HISTOGRAM, dto.MetricType_GAUGE_HISTOGRAM:
bb := p.mf.GetMetric()[p.metricPos].GetHistogram().GetBucket()
if p.fieldPos >= len(bb) {
p.fieldsDone = true
return true, model.BucketLabel, "+Inf"
}
b := bb[p.fieldPos]
p.fieldsDone = math.IsInf(b.GetUpperBound(), +1)
return true, model.BucketLabel, formatOpenMetricsFloat(b.GetUpperBound())
}
return false, "", ""
}
var errInvalidVarint = errors.New("protobufparse: invalid varint encountered")
// readDelimited is essentially doing what the function of the same name in
// github.com/matttproud/golang_protobuf_extensions/pbutil is doing, but it is
// specific to a MetricFamily, utilizes the more efficient gogo-protobuf
// unmarshaling, and acts on a byte slice directly without any additional
// staging buffers.
func readDelimited(b []byte, mf *dto.MetricFamily) (n int, err error) {
if len(b) == 0 {
return 0, io.EOF
}
messageLength, varIntLength := proto.DecodeVarint(b)
if varIntLength == 0 || varIntLength > binary.MaxVarintLen32 {
return 0, errInvalidVarint
}
totalLength := varIntLength + int(messageLength)
if totalLength > len(b) {
return 0, errors.Errorf("protobufparse: insufficient length of buffer, expected at least %d bytes, got %d bytes", totalLength, len(b))
}
mf.Reset()
return totalLength, mf.Unmarshal(b[varIntLength:totalLength])
}
// formatOpenMetricsFloat works like the usual Go string formatting of a fleat
// but appends ".0" if the resulting number would otherwise contain neither a
// "." nor an "e".
func formatOpenMetricsFloat(f float64) string {
// A few common cases hardcoded.
switch {
case f == 1:
return "1.0"
case f == 0:
return "0.0"
case f == -1:
return "-1.0"
case math.IsNaN(f):
return "NaN"
case math.IsInf(f, +1):
return "+Inf"
case math.IsInf(f, -1):
return "-Inf"
}
s := fmt.Sprint(f)
if strings.ContainsAny(s, "e.") {
return s
}
return s + ".0"
}
// isNativeHistogram returns false iff the provided histograms has no sparse
// buckets and a zero threshold of 0 and a zero count of 0. In principle, this
// could still be meant to be a native histogram (with a zero threshold of 0 and
// no observations yet), but for now, we'll treat this case as a conventional
// histogram.
//
// TODO(beorn7): In the final format, there should be an unambiguous way of
// deciding if a histogram should be ingested as a conventional one or a native
// one.
func isNativeHistogram(h *dto.Histogram) bool {
return len(h.GetNegativeDelta()) > 0 ||
len(h.GetPositiveDelta()) > 0 ||
h.GetZeroCount() > 0 ||
h.GetZeroThreshold() > 0
}