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helper.go
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helper.go
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// Copyright The OpenTelemetry 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:https://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 prometheusremotewriteexporter
import (
"errors"
"log"
"math"
"sort"
"strconv"
"strings"
"time"
"unicode"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/pkg/timestamp"
"github.com/prometheus/prometheus/prompb"
"go.opentelemetry.io/collector/model/pdata"
)
const (
nameStr = "__name__"
sumStr = "_sum"
countStr = "_count"
bucketStr = "_bucket"
leStr = "le"
quantileStr = "quantile"
pInfStr = "+Inf"
keyStr = "key"
)
type bucketBoundsData struct {
sig string
bound float64
}
// byBucketBoundsData enables the usage of sort.Sort() with a slice of bucket bounds
type byBucketBoundsData []bucketBoundsData
func (m byBucketBoundsData) Len() int { return len(m) }
func (m byBucketBoundsData) Less(i, j int) bool { return m[i].bound < m[j].bound }
func (m byBucketBoundsData) Swap(i, j int) { m[i], m[j] = m[j], m[i] }
// ByLabelName enables the usage of sort.Sort() with a slice of labels
type ByLabelName []prompb.Label
func (a ByLabelName) Len() int { return len(a) }
func (a ByLabelName) Less(i, j int) bool { return a[i].Name < a[j].Name }
func (a ByLabelName) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
// validateMetrics returns a bool representing whether the metric has a valid type and temporality combination and a
// matching metric type and field
func validateMetrics(metric pdata.Metric) bool {
switch metric.DataType() {
case pdata.MetricDataTypeGauge:
return metric.Gauge().DataPoints().Len() != 0
case pdata.MetricDataTypeSum:
return metric.Sum().DataPoints().Len() != 0 && metric.Sum().AggregationTemporality() == pdata.MetricAggregationTemporalityCumulative
case pdata.MetricDataTypeHistogram:
return metric.Histogram().DataPoints().Len() != 0 && metric.Histogram().AggregationTemporality() == pdata.MetricAggregationTemporalityCumulative
case pdata.MetricDataTypeSummary:
return metric.Summary().DataPoints().Len() != 0
}
return false
}
// addSample finds a TimeSeries in tsMap that corresponds to the label set labels, and add sample to the TimeSeries; it
// creates a new TimeSeries in the map if not found and returns the time series signature.
// tsMap will be unmodified if either labels or sample is nil, but can still be modified if the exemplar is nil.
func addSample(tsMap map[string]*prompb.TimeSeries, sample *prompb.Sample, labels []prompb.Label,
metric pdata.Metric) string {
if sample == nil || labels == nil || tsMap == nil {
return ""
}
sig := timeSeriesSignature(metric, &labels)
ts, ok := tsMap[sig]
if ok {
ts.Samples = append(ts.Samples, *sample)
} else {
newTs := &prompb.TimeSeries{
Labels: labels,
Samples: []prompb.Sample{*sample},
}
tsMap[sig] = newTs
}
return sig
}
// addExemplars finds a bucket bound that corresponds to the exemplars value and add the exemplar to the specific sig;
// we only add exemplars if samples are presents
// tsMap is unmodified if either of its parameters is nil and samples are nil.
func addExemplars(tsMap map[string]*prompb.TimeSeries, exemplars []prompb.Exemplar, bucketBoundsData []bucketBoundsData) {
if tsMap == nil || bucketBoundsData == nil || exemplars == nil {
return
}
sort.Sort(byBucketBoundsData(bucketBoundsData))
for _, exemplar := range exemplars {
addExemplar(tsMap, bucketBoundsData, exemplar)
}
}
func addExemplar(tsMap map[string]*prompb.TimeSeries, bucketBounds []bucketBoundsData, exemplar prompb.Exemplar) {
for _, bucketBound := range bucketBounds {
sig := bucketBound.sig
bound := bucketBound.bound
_, ok := tsMap[sig]
if ok {
if tsMap[sig].Samples != nil {
if tsMap[sig].Exemplars == nil {
tsMap[sig].Exemplars = make([]prompb.Exemplar, 0)
}
if exemplar.Value <= bound {
tsMap[sig].Exemplars = append(tsMap[sig].Exemplars, exemplar)
return
}
}
}
}
}
// timeSeries return a string signature in the form of:
// TYPE-label1-value1- ... -labelN-valueN
// the label slice should not contain duplicate label names; this method sorts the slice by label name before creating
// the signature.
func timeSeriesSignature(metric pdata.Metric, labels *[]prompb.Label) string {
b := strings.Builder{}
b.WriteString(metric.DataType().String())
sort.Sort(ByLabelName(*labels))
for _, lb := range *labels {
b.WriteString("-")
b.WriteString(lb.GetName())
b.WriteString("-")
b.WriteString(lb.GetValue())
}
return b.String()
}
// createAttributes creates a slice of Cortex Label with OTLP attributes and pairs of string values.
// Unpaired string value is ignored. String pairs overwrites OTLP labels if collision happens, and the overwrite is
// logged. Resultant label names are sanitized.
func createAttributes(resource pdata.Resource, attributes pdata.AttributeMap, externalLabels map[string]string, extras ...string) []prompb.Label {
// map ensures no duplicate label name
l := map[string]prompb.Label{}
for key, value := range externalLabels {
// External labels have already been sanitized
l[key] = prompb.Label{
Name: key,
Value: value,
}
}
resource.Attributes().Range(func(key string, value pdata.AttributeValue) bool {
if isUsefulResourceAttribute(key) {
l[key] = prompb.Label{
Name: sanitize(key),
Value: value.StringVal(), // TODO(jbd): Decide what to do with non-string attributes.
}
}
return true
})
attributes.Range(func(key string, value pdata.AttributeValue) bool {
l[key] = prompb.Label{
Name: sanitize(key),
Value: value.AsString(),
}
return true
})
for i := 0; i < len(extras); i += 2 {
if i+1 >= len(extras) {
break
}
_, found := l[extras[i]]
if found {
log.Println("label " + extras[i] + " is overwritten. Check if Prometheus reserved labels are used.")
}
// internal labels should be maintained
name := extras[i]
if !(len(name) > 4 && name[:2] == "__" && name[len(name)-2:] == "__") {
name = sanitize(name)
}
l[extras[i]] = prompb.Label{
Name: name,
Value: extras[i+1],
}
}
s := make([]prompb.Label, 0, len(l))
for _, lb := range l {
s = append(s, lb)
}
return s
}
func isUsefulResourceAttribute(key string) bool {
// TODO(jbd): Allow users to configure what other resource
// attributes to be included.
// Decide what to do with non-string attributes.
// We should always output "job" and "instance".
switch key {
case model.InstanceLabel:
return true
case model.JobLabel:
return true
}
return false
}
// getPromMetricName creates a Prometheus metric name by attaching namespace prefix for Monotonic metrics.
func getPromMetricName(metric pdata.Metric, ns string) string {
name := metric.Name()
if len(ns) > 0 {
name = ns + "_" + name
}
return sanitize(name)
}
// batchTimeSeries splits series into multiple batch write requests.
func batchTimeSeries(tsMap map[string]*prompb.TimeSeries, maxBatchByteSize int) ([]*prompb.WriteRequest, error) {
if len(tsMap) == 0 {
return nil, errors.New("invalid tsMap: cannot be empty map")
}
var requests []*prompb.WriteRequest
var tsArray []prompb.TimeSeries
sizeOfCurrentBatch := 0
for _, v := range tsMap {
sizeOfSeries := v.Size()
if sizeOfCurrentBatch+sizeOfSeries >= maxBatchByteSize {
wrapped := convertTimeseriesToRequest(tsArray)
requests = append(requests, wrapped)
tsArray = make([]prompb.TimeSeries, 0)
sizeOfCurrentBatch = 0
}
tsArray = append(tsArray, *v)
sizeOfCurrentBatch += sizeOfSeries
}
if len(tsArray) != 0 {
wrapped := convertTimeseriesToRequest(tsArray)
requests = append(requests, wrapped)
}
return requests, nil
}
// convertTimeStamp converts OTLP timestamp in ns to timestamp in ms
func convertTimeStamp(timestamp pdata.Timestamp) int64 {
return timestamp.AsTime().UnixNano() / (int64(time.Millisecond) / int64(time.Nanosecond))
}
// copied from prometheus-go-metric-exporter
// sanitize replaces non-alphanumeric characters with underscores in s.
func sanitize(s string) string {
if len(s) == 0 {
return s
}
// Note: No length limit for label keys because Prometheus doesn't
// define a length limit, thus we should NOT be truncating label keys.
// See https://github.com/orijtech/prometheus-go-metrics-exporter/issues/4.
s = strings.Map(sanitizeRune, s)
if unicode.IsDigit(rune(s[0])) {
s = keyStr + "_" + s
}
if s[0] == '_' {
s = keyStr + s
}
return s
}
// copied from prometheus-go-metric-exporter
// sanitizeRune converts anything that is not a letter or digit to an underscore
func sanitizeRune(r rune) rune {
if unicode.IsLetter(r) || unicode.IsDigit(r) {
return r
}
// Everything else turns into an underscore
return '_'
}
// addSingleNumberDataPoint converts the metric value stored in pt to a Prometheus sample, and add the sample
// to its corresponding time series in tsMap
func addSingleNumberDataPoint(pt pdata.NumberDataPoint, resource pdata.Resource, metric pdata.Metric, namespace string,
tsMap map[string]*prompb.TimeSeries, externalLabels map[string]string) {
// create parameters for addSample
name := getPromMetricName(metric, namespace)
labels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, name)
sample := &prompb.Sample{
// convert ns to ms
Timestamp: convertTimeStamp(pt.Timestamp()),
}
switch pt.Type() {
case pdata.MetricValueTypeInt:
sample.Value = float64(pt.IntVal())
case pdata.MetricValueTypeDouble:
sample.Value = pt.DoubleVal()
}
addSample(tsMap, sample, labels, metric)
}
// addSingleHistogramDataPoint converts pt to 2 + min(len(ExplicitBounds), len(BucketCount)) + 1 samples. It
// ignore extra buckets if len(ExplicitBounds) > len(BucketCounts)
func addSingleHistogramDataPoint(pt pdata.HistogramDataPoint, resource pdata.Resource, metric pdata.Metric, namespace string,
tsMap map[string]*prompb.TimeSeries, externalLabels map[string]string) {
time := convertTimeStamp(pt.Timestamp())
// sum, count, and buckets of the histogram should append suffix to baseName
baseName := getPromMetricName(metric, namespace)
// treat sum as a sample in an individual TimeSeries
sum := &prompb.Sample{
Value: pt.Sum(),
Timestamp: time,
}
sumlabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+sumStr)
addSample(tsMap, sum, sumlabels, metric)
// treat count as a sample in an individual TimeSeries
count := &prompb.Sample{
Value: float64(pt.Count()),
Timestamp: time,
}
countlabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+countStr)
addSample(tsMap, count, countlabels, metric)
// cumulative count for conversion to cumulative histogram
var cumulativeCount uint64
promExemplars := getPromExemplars(pt)
bucketBounds := make([]bucketBoundsData, 0)
// process each bound, based on histograms proto definition, # of buckets = # of explicit bounds + 1
for index, bound := range pt.ExplicitBounds() {
if index >= len(pt.BucketCounts()) {
break
}
cumulativeCount += pt.BucketCounts()[index]
bucket := &prompb.Sample{
Value: float64(cumulativeCount),
Timestamp: time,
}
boundStr := strconv.FormatFloat(bound, 'f', -1, 64)
labels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+bucketStr, leStr, boundStr)
sig := addSample(tsMap, bucket, labels, metric)
bucketBounds = append(bucketBounds, bucketBoundsData{sig: sig, bound: bound})
}
// add le=+Inf bucket
cumulativeCount += pt.BucketCounts()[len(pt.BucketCounts())-1]
infBucket := &prompb.Sample{
Value: float64(cumulativeCount),
Timestamp: time,
}
infLabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+bucketStr, leStr, pInfStr)
sig := addSample(tsMap, infBucket, infLabels, metric)
bucketBounds = append(bucketBounds, bucketBoundsData{sig: sig, bound: math.Inf(1)})
addExemplars(tsMap, promExemplars, bucketBounds)
}
func getPromExemplars(pt pdata.HistogramDataPoint) []prompb.Exemplar {
var promExemplars []prompb.Exemplar
for i := 0; i < pt.Exemplars().Len(); i++ {
exemplar := pt.Exemplars().At(i)
promExemplar := &prompb.Exemplar{
Value: exemplar.DoubleVal(),
Timestamp: timestamp.FromTime(exemplar.Timestamp().AsTime()),
}
exemplar.FilteredAttributes().Range(func(key string, value pdata.AttributeValue) bool {
promLabel := prompb.Label{
Name: key,
Value: value.AsString(),
}
promExemplar.Labels = append(promExemplar.Labels, promLabel)
return true
})
promExemplars = append(promExemplars, *promExemplar)
}
return promExemplars
}
// addSingleSummaryDataPoint converts pt to len(QuantileValues) + 2 samples.
func addSingleSummaryDataPoint(pt pdata.SummaryDataPoint, resource pdata.Resource, metric pdata.Metric, namespace string,
tsMap map[string]*prompb.TimeSeries, externalLabels map[string]string) {
time := convertTimeStamp(pt.Timestamp())
// sum and count of the summary should append suffix to baseName
baseName := getPromMetricName(metric, namespace)
// treat sum as a sample in an individual TimeSeries
sum := &prompb.Sample{
Value: pt.Sum(),
Timestamp: time,
}
sumlabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+sumStr)
addSample(tsMap, sum, sumlabels, metric)
// treat count as a sample in an individual TimeSeries
count := &prompb.Sample{
Value: float64(pt.Count()),
Timestamp: time,
}
countlabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName+countStr)
addSample(tsMap, count, countlabels, metric)
// process each percentile/quantile
for i := 0; i < pt.QuantileValues().Len(); i++ {
qt := pt.QuantileValues().At(i)
quantile := &prompb.Sample{
Value: qt.Value(),
Timestamp: time,
}
percentileStr := strconv.FormatFloat(qt.Quantile(), 'f', -1, 64)
qtlabels := createAttributes(resource, pt.Attributes(), externalLabels, nameStr, baseName, quantileStr, percentileStr)
addSample(tsMap, quantile, qtlabels, metric)
}
}
func orderBySampleTimestamp(tsArray []prompb.TimeSeries) []prompb.TimeSeries {
for i := range tsArray {
sL := tsArray[i].Samples
sort.Slice(sL, func(i, j int) bool {
return sL[i].Timestamp < sL[j].Timestamp
})
}
return tsArray
}
func convertTimeseriesToRequest(tsArray []prompb.TimeSeries) *prompb.WriteRequest {
// the remote_write endpoint only requires the timeseries.
// otlp defines it's own way to handle metric metadata
return &prompb.WriteRequest{
// Prometheus requires time series to be sorted by Timestamp to avoid out of order problems.
// See:
// * https://github.com/open-telemetry/wg-prometheus/issues/10
// * https://github.com/open-telemetry/opentelemetry-collector/issues/2315
Timeseries: orderBySampleTimestamp(tsArray),
}
}