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histograms: Move to new exposition protobuf format

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This is an incompatible protobuf change. Instrumented targets must
include https://github.com/prometheus/client_golang/pull/1092 to make
this work.

Signed-off-by: beorn7 <beorn@grafana.com>
This commit is contained in:
beorn7 2022-07-19 18:11:33 +02:00
parent 5937b4f5d4
commit c40b105efd
4 changed files with 728 additions and 719 deletions
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50
model/textparse/protobufparse.go
View file

@ -38,7 +38,7 @@ import (
// 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 sparse histograms), we have to revisit the
// 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
@ -132,8 +132,8 @@ func (p *ProtobufParser) Series() ([]byte, *int64, float64) {
return p.metricBytes.Bytes(), nil, v
}
// Histogram returns the bytes of a series with a sparse histogram as a
// value, the timestamp if set, and the sparse histogram in the current
// 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.
func (p *ProtobufParser) Histogram() ([]byte, *int64, *histogram.Histogram) {
var (
@ -144,19 +144,19 @@ func (p *ProtobufParser) Histogram() ([]byte, *int64, *histogram.Histogram) {
sh := histogram.Histogram{
Count: h.GetSampleCount(),
Sum: h.GetSampleSum(),
ZeroThreshold: h.GetSbZeroThreshold(),
ZeroCount: h.GetSbZeroCount(),
Schema: h.GetSbSchema(),
PositiveSpans: make([]histogram.Span, len(h.GetSbPositive().GetSpan())),
PositiveBuckets: h.GetSbPositive().GetDelta(),
NegativeSpans: make([]histogram.Span, len(h.GetSbNegative().GetSpan())),
NegativeBuckets: h.GetSbNegative().GetDelta(),
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.GetSbPositive().GetSpan() {
for i, span := range h.GetPositiveSpan() {
sh.PositiveSpans[i].Offset = span.GetOffset()
sh.PositiveSpans[i].Length = span.GetLength()
}
for i, span := range h.GetSbNegative().GetSpan() {
for i, span := range h.GetNegativeSpan() {
sh.NegativeSpans[i].Offset = span.GetOffset()
sh.NegativeSpans[i].Length = span.GetLength()
}
@ -231,7 +231,7 @@ func (p *ProtobufParser) Metric(l *labels.Labels) 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 sparse
// 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 {
@ -246,7 +246,7 @@ func (p *ProtobufParser) Exemplar(ex *exemplar.Exemplar) bool {
if p.state == EntrySeries {
return false // At _count or _sum.
}
p.fieldPos = 0 // Start at 1st bucket for sparse histograms.
p.fieldPos = 0 // Start at 1st bucket for native histograms.
}
for p.fieldPos < len(bb) {
exProto = bb[p.fieldPos].GetExemplar()
@ -314,7 +314,7 @@ func (p *ProtobufParser) Next() (Entry, error) {
p.state = EntryType
case EntryType:
if p.mf.GetType() == dto.MetricType_HISTOGRAM &&
isSparseHistogram(p.mf.GetMetric()[0].GetHistogram()) {
isNativeHistogram(p.mf.GetMetric()[0].GetHistogram()) {
p.state = EntryHistogram
} else {
p.state = EntrySeries
@ -465,18 +465,18 @@ func formatOpenMetricsFloat(f float64) string {
return s + ".0"
}
// isSparseHistogram returns false iff the provided histograms has no
// SparseBuckets and a zero threshold of 0 and a zero count of 0. In principle,
// this could still be meant to be a sparse histgram (with a zero threshold of 0
// and no observations yet), but for now, we'll treat this case as a conventional
// 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 sparse
// deciding if a histogram should be ingested as a conventional one or a native
// one.
func isSparseHistogram(h *dto.Histogram) bool {
return len(h.GetSbNegative().GetDelta()) > 0 ||
len(h.GetSbPositive().GetDelta()) > 0 ||
h.GetSbZeroCount() > 0 ||
h.GetSbZeroThreshold() > 0
func isNativeHistogram(h *dto.Histogram) bool {
return len(h.GetNegativeDelta()) > 0 ||
len(h.GetPositiveDelta()) > 0 ||
h.GetZeroCount() > 0 ||
h.GetZeroThreshold() > 0
}

40
model/textparse/protobufparse_test.go
View file

@ -122,38 +122,34 @@ metric: <
value: -0.00029
>
>
sb_schema: 3
sb_zero_threshold: 2.938735877055719e-39
sb_zero_count: 2
sb_negative: <
span: <
schema: 3
zero_threshold: 2.938735877055719e-39
zero_count: 2
negative_span: <
offset: -162
length: 1
>
span: <
negative_span: <
offset: 23
length: 4
>
delta: 1
delta: 3
delta: -2
delta: -1
delta: 1
>
sb_positive: <
span: <
negative_delta: 1
negative_delta: 3
negative_delta: -2
negative_delta: -1
negative_delta: 1
positive_span: <
offset: -161
length: 1
>
span: <
positive_span: <
offset: 8
length: 3
>
delta: 1
delta: 2
delta: -1
delta: -1
>
positive_delta: 1
positive_delta: 2
positive_delta: -1
positive_delta: -1
>
timestamp_ms: 1234568
>
@ -196,8 +192,8 @@ metric: <
value: -0.000295
>
>
sb_schema: 0
sb_zero_threshold: 0
schema: 0
zero_threshold: 0
>
>

1247
prompb/io/prometheus/client/metrics.pb.go
View file

File diff suppressed because it is too large Load diff

56
prompb/io/prometheus/client/metrics.proto
View file

@ -69,21 +69,38 @@ message Untyped {
message Histogram {
uint64 sample_count = 1;
double sample_count_float = 9; // Overrides sample_count if > 0.
double sample_count_float = 4; // Overrides sample_count if > 0.
double sample_sum = 2;
// Buckets for the conventional histogram.
repeated Bucket bucket = 3; // Ordered in increasing order of upper_bound, +Inf bucket is optional.
// Sparse bucket (sb) stuff:
// The sb_schema defines the bucket schema. Currently, valid numbers are -4 <= n <= 8.
// Everything below here is for native histograms (also known as sparse histograms).
// schema defines the bucket schema. Currently, valid numbers are -4 <= n <= 8.
// They are all for base-2 bucket schemas, where 1 is a bucket boundary in each case, and
// then each power of two is divided into 2^n logarithmic buckets.
// Or in other words, each bucket boundary is the previous boundary times 2^(2^-n).
// In the future, more bucket schemas may be added using numbers < -4 or > 8.
sint32 sb_schema = 4;
double sb_zero_threshold = 5; // Breadth of the zero bucket.
uint64 sb_zero_count = 6; // Count in zero bucket.
double sb_zero_count_float = 10; // Overrides sb_zero_count if > 0.
SparseBuckets sb_negative = 7; // Negative sparse buckets.
SparseBuckets sb_positive = 8; // Positive sparse buckets.
sint32 schema = 5;
double zero_threshold = 6; // Breadth of the zero bucket.
uint64 zero_count = 7; // Count in zero bucket.
double zero_count_float = 8; // Overrides sb_zero_count if > 0.
// Negative buckets for the native histogram.
repeated BucketSpan negative_span = 9;
// Use either "negative_delta" or "negative_count", the former for
// regular histograms with integer counts, the latter for float
// histograms.
repeated sint64 negative_delta = 10; // Count delta of each bucket compared to previous one (or to zero for 1st bucket).
repeated double negative_count = 11; // Absolute count of each bucket.
// Positive buckets for the native histogram.
repeated BucketSpan positive_span = 12;
// Use either "positive_delta" or "positive_count", the former for
// regular histograms with integer counts, the latter for float
// histograms.
repeated sint64 positive_delta = 13; // Count delta of each bucket compared to previous one (or to zero for 1st bucket).
repeated double positive_count = 14; // Absolute count of each bucket.
}
message Bucket {
@ -93,23 +110,16 @@ message Bucket {
Exemplar exemplar = 3;
}
message SparseBuckets {
// A Span is a given number of consecutive buckets at a given
// offset. Logically, it would be more straightforward to include
// the bucket counts in the Span. However, the protobuf
// representation is more compact in the way the data is structured
// here (with all the buckets in a single array separate from the
// Spans).
message Span {
// A BucketSpan defines a number of consecutive buckets in a native
// histogram with their offset. Logically, it would be more
// straightforward to include the bucket counts in the Span. However,
// the protobuf representation is more compact in the way the data is
// structured here (with all the buckets in a single array separate
// from the Spans).
message BucketSpan {
sint32 offset = 1; // Gap to previous span, or starting point for 1st span (which can be negative).
uint32 length = 2; // Length of consecutive buckets.
}
repeated Span span = 1;
// Only one of "delta" or "count" may be used, the former for regular
// histograms with integer counts, the latter for float histograms.
repeated sint64 delta = 2; // Count delta of each bucket compared to previous one (or to zero for 1st bucket).
repeated double count = 3; // Absolute count of each bucket.
}
message Exemplar {
repeated LabelPair label = 1;