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[chore] Simplify the OTEL setup (#4110)

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# Description

This simplifies our OTEL setup by:

* Getting rid of some deprecated things.
* Using `autoexport` and letting things get configured by the `OTEL_` environment variables.
* Removing all the unnecessary config options.

## Checklist

Please put an x inside each checkbox to indicate that you've read and followed it: `[ ]` -> `[x]`

If this is a documentation change, only the first checkbox must be filled (you can delete the others if you want).

- [x] I/we have read the [GoToSocial contribution guidelines](https://codeberg.org/superseriousbusiness/gotosocial/src/branch/main/CONTRIBUTING.md).
- [x] I/we have discussed the proposed changes already, either in an issue on the repository, or in the Matrix chat.
- [x] I/we have not leveraged AI to create the proposed changes.
- [x] I/we have performed a self-review of added code.
- [x] I/we have written code that is legible and maintainable by others.
- [ ] I/we have commented the added code, particularly in hard-to-understand areas.
- [x] I/we have made any necessary changes to documentation.
- [ ] I/we have added tests that cover new code.
- [x] I/we have run tests and they pass locally with the changes.
- [x] I/we have run `go fmt ./...` and `golangci-lint run`.

Reviewed-on: https://codeberg.org/superseriousbusiness/gotosocial/pulls/4110
Reviewed-by: tobi <kipvandenbos@noreply.codeberg.org>
Co-authored-by: Daenney <daenney@noreply.codeberg.org>
Co-committed-by: Daenney <daenney@noreply.codeberg.org>
This commit is contained in:
Daenney 2025-05-05 16:22:45 +00:00 • committed by tobi
commit ecbdc4227b
145 changed files with 21740 additions and 1319 deletions
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## Summary
Using the Prometheus bridge and the OTLP exporter adds roughly ~50% to the CPU and memory overhead of an application compared to serving a Prometheus HTTP endpoint for metrics.
However, unless the application has extremely high cardinality for metrics, this is unlikely to represent a significant amount of additional overhead because the base-line memory consumption of client libraries is relatively low. For an application with 30k timeseries (which is a very high number), the additional overhead is about 50MB and about 0.1 CPU cores.
The bridge is particularly useful if you are exporting to an OpenTelemetry Collector, since the OTLP receiver is much more efficient than the Prometheus receiver. For the same 30k timeseries, the Prometheus receiver uses 3x the amount of memory, and 20x the amount of CPU. In concrete numbers, this is an additional 228 MB of memory, and 0.57 CPU cores.
For an application using the Prometheus client library, and exporting to an OpenTelemetry collector, the total CPU usage is 55% lower and total memory usage is 45% lower when using the bridge and the OTLP receiver compared to using a Prometheus endpoint and the collector's Prometheus receiver.
## Methods and Results
The sample application uses the Prometheus client library, and defines one histogram with the default 12 buckets, one counter, and one gauge. Each metric has a single label with 10k values, which are observed every second. See the [sample application's source](https://github.com/dashpole/client_golang/pull/1).
The memory usage of the sample application is measured using the `/memory/classes/total:bytes` metric from the go runtime. The CPU usage of the application is measured using `top`. The CPU and memory usage of the collector are measured using `docker stats`. It was built using v0.50.0 of the bridge, v1.25.0 of the OpenTelemetry API and SDK, and v1.19.0 of the Prometheus client.
The OpenTelemetry Collector is configured with only the OTLP or Prometheus receiver, and the debug (logging) exporter with only the basic output. The benchmark uses the Contrib distribution at v0.97.0.
| Experiment | Memory Usage (MB) | CPU Usage (millicores) |
|---|---|---|
| App w/ Prometheus Export | 94 | 220 |
| App w/ Bridge + OTLP Export | 140 | 330 |
| Collector w/ Prometheus Receiver | 320 | 600 |
| Collector w/ OTLP Receiver | 92 | 30 |

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package prometheus // import "go.opentelemetry.io/contrib/bridges/prometheus"
import (
"github.com/prometheus/client_golang/prometheus"
)
// config contains options for the producer.
type config struct {
gatherers []prometheus.Gatherer
}
// newConfig creates a validated config configured with options.
func newConfig(opts ...Option) config {
cfg := config{}
for _, opt := range opts {
cfg = opt.apply(cfg)
}
if len(cfg.gatherers) == 0 {
cfg.gatherers = []prometheus.Gatherer{prometheus.DefaultGatherer}
}
return cfg
}
// Option sets producer option values.
type Option interface {
apply(config) config
}
type optionFunc func(config) config
func (fn optionFunc) apply(cfg config) config {
return fn(cfg)
}
// WithGatherer configures which prometheus Gatherer the Bridge will gather
// from. If no registerer is used the prometheus DefaultGatherer is used.
func WithGatherer(gatherer prometheus.Gatherer) Option {
return optionFunc(func(cfg config) config {
cfg.gatherers = append(cfg.gatherers, gatherer)
return cfg
})
}

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
// Package prometheus provides a bridge from Prometheus to OpenTelemetry.
//
// The Prometheus Bridge allows using the [Prometheus Golang client library]
// with the OpenTelemetry SDK. This enables prometheus instrumentation libraries
// to be used with OpenTelemetry exporters, including OTLP.
//
// Prometheus histograms are translated to OpenTelemetry exponential histograms
// when native histograms are enabled in the Prometheus client. To enable
// Prometheus native histograms, set the (currently experimental) NativeHistogram...
// options of the prometheus [HistogramOpts] when creating prometheus histograms.
//
// While the Prometheus Bridge has some overhead, it can significantly reduce the
// combined overall CPU and Memory footprint when sending to an OpenTelemetry
// Collector. See the [benchmarks] for more details.
//
// [Prometheus Golang client library]: https://github.com/prometheus/client_golang
// [HistogramOpts]: https://pkg.go.dev/github.com/prometheus/client_golang/prometheus#HistogramOpts
// [benchmarks]: https://github.com/open-telemetry/opentelemetry-go-contrib/blob/main/bridges/prometheus/BENCHMARKS.md
package prometheus // import "go.opentelemetry.io/contrib/bridges/prometheus"

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package prometheus // import "go.opentelemetry.io/contrib/bridges/prometheus"
import (
"context"
"errors"
"fmt"
"math"
"strings"
"time"
"github.com/prometheus/client_golang/prometheus"
dto "github.com/prometheus/client_model/go"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/instrumentation"
"go.opentelemetry.io/otel/sdk/metric"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
const (
scopeName = "go.opentelemetry.io/contrib/bridges/prometheus"
traceIDLabel = "trace_id"
spanIDLabel = "span_id"
)
var (
errUnsupportedType = errors.New("unsupported metric type")
processStartTime = time.Now()
)
type producer struct {
gatherers prometheus.Gatherers
}
// NewMetricProducer returns a metric.Producer that fetches metrics from
// Prometheus. This can be used to allow Prometheus instrumentation to be
// added to an OpenTelemetry export pipeline.
func NewMetricProducer(opts ...Option) metric.Producer {
cfg := newConfig(opts...)
return &producer{
gatherers: cfg.gatherers,
}
}
func (p *producer) Produce(context.Context) ([]metricdata.ScopeMetrics, error) {
now := time.Now()
var errs multierr
otelMetrics := make([]metricdata.Metrics, 0)
for _, gatherer := range p.gatherers {
promMetrics, err := gatherer.Gather()
if err != nil {
errs = append(errs, err)
continue
}
m, err := convertPrometheusMetricsInto(promMetrics, now)
otelMetrics = append(otelMetrics, m...)
if err != nil {
errs = append(errs, err)
}
}
if errs.errOrNil() != nil {
otel.Handle(errs.errOrNil())
}
if len(otelMetrics) == 0 {
return nil, nil
}
return []metricdata.ScopeMetrics{{
Scope: instrumentation.Scope{
Name: scopeName,
},
Metrics: otelMetrics,
}}, nil
}
func convertPrometheusMetricsInto(promMetrics []*dto.MetricFamily, now time.Time) ([]metricdata.Metrics, error) {
var errs multierr
otelMetrics := make([]metricdata.Metrics, 0)
for _, pm := range promMetrics {
if len(pm.GetMetric()) == 0 {
// This shouldn't ever happen
continue
}
newMetric := metricdata.Metrics{
Name: pm.GetName(),
Description: pm.GetHelp(),
}
switch pm.GetType() {
case dto.MetricType_GAUGE:
newMetric.Data = convertGauge(pm.GetMetric(), now)
case dto.MetricType_COUNTER:
newMetric.Data = convertCounter(pm.GetMetric(), now)
case dto.MetricType_SUMMARY:
newMetric.Data = convertSummary(pm.GetMetric(), now)
case dto.MetricType_HISTOGRAM:
if isExponentialHistogram(pm.GetMetric()[0].GetHistogram()) {
newMetric.Data = convertExponentialHistogram(pm.GetMetric(), now)
} else {
newMetric.Data = convertHistogram(pm.GetMetric(), now)
}
default:
// MetricType_GAUGE_HISTOGRAM, MetricType_UNTYPED
errs = append(errs, fmt.Errorf("%w: %v for metric %v", errUnsupportedType, pm.GetType(), pm.GetName()))
continue
}
otelMetrics = append(otelMetrics, newMetric)
}
return otelMetrics, errs.errOrNil()
}
func isExponentialHistogram(hist *dto.Histogram) bool {
// The prometheus go client ensures at least one of these is non-zero
// so it can be distinguished from a fixed-bucket histogram.
// https://github.com/prometheus/client_golang/blob/7ac90362b02729a65109b33d172bafb65d7dab50/prometheus/histogram.go#L818
return hist.GetZeroThreshold() > 0 ||
hist.GetZeroCount() > 0 ||
len(hist.GetPositiveSpan()) > 0 ||
len(hist.GetNegativeSpan()) > 0
}
func convertGauge(metrics []*dto.Metric, now time.Time) metricdata.Gauge[float64] {
otelGauge := metricdata.Gauge[float64]{
DataPoints: make([]metricdata.DataPoint[float64], len(metrics)),
}
for i, m := range metrics {
dp := metricdata.DataPoint[float64]{
Attributes: convertLabels(m.GetLabel()),
Time: now,
Value: m.GetGauge().GetValue(),
}
if m.GetTimestampMs() != 0 {
dp.Time = time.UnixMilli(m.GetTimestampMs())
}
otelGauge.DataPoints[i] = dp
}
return otelGauge
}
func convertCounter(metrics []*dto.Metric, now time.Time) metricdata.Sum[float64] {
otelCounter := metricdata.Sum[float64]{
DataPoints: make([]metricdata.DataPoint[float64], len(metrics)),
Temporality: metricdata.CumulativeTemporality,
IsMonotonic: true,
}
for i, m := range metrics {
dp := metricdata.DataPoint[float64]{
Attributes: convertLabels(m.GetLabel()),
StartTime: processStartTime,
Time: now,
Value: m.GetCounter().GetValue(),
}
if ex := m.GetCounter().GetExemplar(); ex != nil {
dp.Exemplars = []metricdata.Exemplar[float64]{convertExemplar(ex)}
}
createdTs := m.GetCounter().GetCreatedTimestamp()
if createdTs.IsValid() {
dp.StartTime = createdTs.AsTime()
}
if m.GetTimestampMs() != 0 {
dp.Time = time.UnixMilli(m.GetTimestampMs())
}
otelCounter.DataPoints[i] = dp
}
return otelCounter
}
func convertExponentialHistogram(metrics []*dto.Metric, now time.Time) metricdata.ExponentialHistogram[float64] {
otelExpHistogram := metricdata.ExponentialHistogram[float64]{
DataPoints: make([]metricdata.ExponentialHistogramDataPoint[float64], len(metrics)),
Temporality: metricdata.CumulativeTemporality,
}
for i, m := range metrics {
dp := metricdata.ExponentialHistogramDataPoint[float64]{
Attributes: convertLabels(m.GetLabel()),
StartTime: processStartTime,
Time: now,
Count: m.GetHistogram().GetSampleCount(),
Sum: m.GetHistogram().GetSampleSum(),
Scale: m.GetHistogram().GetSchema(),
ZeroCount: m.GetHistogram().GetZeroCount(),
ZeroThreshold: m.GetHistogram().GetZeroThreshold(),
PositiveBucket: convertExponentialBuckets(
m.GetHistogram().GetPositiveSpan(),
m.GetHistogram().GetPositiveDelta(),
),
NegativeBucket: convertExponentialBuckets(
m.GetHistogram().GetNegativeSpan(),
m.GetHistogram().GetNegativeDelta(),
),
// TODO: Support exemplars
}
createdTs := m.GetHistogram().GetCreatedTimestamp()
if createdTs.IsValid() {
dp.StartTime = createdTs.AsTime()
}
if t := m.GetTimestampMs(); t != 0 {
dp.Time = time.UnixMilli(t)
}
otelExpHistogram.DataPoints[i] = dp
}
return otelExpHistogram
}
func convertExponentialBuckets(bucketSpans []*dto.BucketSpan, deltas []int64) metricdata.ExponentialBucket {
if len(bucketSpans) == 0 {
return metricdata.ExponentialBucket{}
}
// Prometheus Native Histograms buckets are indexed by upper boundary
// while Exponential Histograms are indexed by lower boundary, the result
// being that the Offset fields are different-by-one.
initialOffset := bucketSpans[0].GetOffset() - 1
// We will have one bucket count for each delta, and zeros for the offsets
// after the initial offset.
lenCounts := len(deltas)
for i, bs := range bucketSpans {
if i != 0 {
lenCounts += int(bs.GetOffset())
}
}
counts := make([]uint64, lenCounts)
deltaIndex := 0
countIndex := int32(0)
count := int64(0)
for i, bs := range bucketSpans {
// Do not insert zeroes if this is the first bucketSpan, since those
// zeroes are accounted for in the Offset field.
if i != 0 {
// Increase the count index by the Offset to insert Offset zeroes
countIndex += bs.GetOffset()
}
for j := uint32(0); j < bs.GetLength(); j++ {
// Convert deltas to the cumulative number of observations
count += deltas[deltaIndex]
deltaIndex++
// count should always be positive after accounting for deltas
if count > 0 {
counts[countIndex] = uint64(count)
}
countIndex++
}
}
return metricdata.ExponentialBucket{
Offset: initialOffset,
Counts: counts,
}
}
func convertHistogram(metrics []*dto.Metric, now time.Time) metricdata.Histogram[float64] {
otelHistogram := metricdata.Histogram[float64]{
DataPoints: make([]metricdata.HistogramDataPoint[float64], len(metrics)),
Temporality: metricdata.CumulativeTemporality,
}
for i, m := range metrics {
bounds, bucketCounts, exemplars := convertBuckets(m.GetHistogram().GetBucket(), m.GetHistogram().GetSampleCount())
dp := metricdata.HistogramDataPoint[float64]{
Attributes: convertLabels(m.GetLabel()),
StartTime: processStartTime,
Time: now,
Count: m.GetHistogram().GetSampleCount(),
Sum: m.GetHistogram().GetSampleSum(),
Bounds: bounds,
BucketCounts: bucketCounts,
Exemplars: exemplars,
}
createdTs := m.GetHistogram().GetCreatedTimestamp()
if createdTs.IsValid() {
dp.StartTime = createdTs.AsTime()
}
if m.GetTimestampMs() != 0 {
dp.Time = time.UnixMilli(m.GetTimestampMs())
}
otelHistogram.DataPoints[i] = dp
}
return otelHistogram
}
func convertBuckets(buckets []*dto.Bucket, sampleCount uint64) ([]float64, []uint64, []metricdata.Exemplar[float64]) {
if len(buckets) == 0 {
// This should never happen
return nil, nil, nil
}
// buckets will only include the +Inf bucket if there is an exemplar for it
// https://github.com/prometheus/client_golang/blob/d038ab96c0c7b9cd217a39072febd610bcdf1fd8/prometheus/metric.go#L189
// we need to handle the case where it is present, or where it is missing.
hasInf := math.IsInf(buckets[len(buckets)-1].GetUpperBound(), +1)
var bounds []float64
var bucketCounts []uint64
if hasInf {
bounds = make([]float64, len(buckets)-1)
bucketCounts = make([]uint64, len(buckets))
} else {
bounds = make([]float64, len(buckets))
bucketCounts = make([]uint64, len(buckets)+1)
}
exemplars := make([]metricdata.Exemplar[float64], 0)
var previousCount uint64
for i, bucket := range buckets {
// The last bound may be the +Inf bucket, which is implied in OTel, but
// is explicit in Prometheus. Skip the last boundary if it is the +Inf
// bound.
if bound := bucket.GetUpperBound(); !math.IsInf(bound, +1) {
bounds[i] = bound
}
previousCount, bucketCounts[i] = bucket.GetCumulativeCount(), bucket.GetCumulativeCount()-previousCount
if ex := bucket.GetExemplar(); ex != nil {
exemplars = append(exemplars, convertExemplar(ex))
}
}
if !hasInf {
// The Inf bucket was missing, so set the last bucket counts to the
// overall count
bucketCounts[len(bucketCounts)-1] = sampleCount - previousCount
}
return bounds, bucketCounts, exemplars
}
func convertSummary(metrics []*dto.Metric, now time.Time) metricdata.Summary {
otelSummary := metricdata.Summary{
DataPoints: make([]metricdata.SummaryDataPoint, len(metrics)),
}
for i, m := range metrics {
dp := metricdata.SummaryDataPoint{
Attributes: convertLabels(m.GetLabel()),
StartTime: processStartTime,
Time: now,
Count: m.GetSummary().GetSampleCount(),
Sum: m.GetSummary().GetSampleSum(),
QuantileValues: convertQuantiles(m.GetSummary().GetQuantile()),
}
createdTs := m.GetSummary().GetCreatedTimestamp()
if createdTs.IsValid() {
dp.StartTime = createdTs.AsTime()
}
if t := m.GetTimestampMs(); t != 0 {
dp.Time = time.UnixMilli(t)
}
otelSummary.DataPoints[i] = dp
}
return otelSummary
}
func convertQuantiles(quantiles []*dto.Quantile) []metricdata.QuantileValue {
otelQuantiles := make([]metricdata.QuantileValue, len(quantiles))
for i, quantile := range quantiles {
dp := metricdata.QuantileValue{
Quantile: quantile.GetQuantile(),
Value: quantile.GetValue(),
}
otelQuantiles[i] = dp
}
return otelQuantiles
}
func convertLabels(labels []*dto.LabelPair) attribute.Set {
kvs := make([]attribute.KeyValue, len(labels))
for i, l := range labels {
kvs[i] = attribute.String(l.GetName(), l.GetValue())
}
return attribute.NewSet(kvs...)
}
func convertExemplar(exemplar *dto.Exemplar) metricdata.Exemplar[float64] {
attrs := make([]attribute.KeyValue, 0)
var traceID, spanID []byte
// find the trace ID and span ID in attributes, if it exists
for _, label := range exemplar.GetLabel() {
if label.GetName() == traceIDLabel {
traceID = []byte(label.GetValue())
} else if label.GetName() == spanIDLabel {
spanID = []byte(label.GetValue())
} else {
attrs = append(attrs, attribute.String(label.GetName(), label.GetValue()))
}
}
return metricdata.Exemplar[float64]{
Value: exemplar.GetValue(),
Time: exemplar.GetTimestamp().AsTime(),
TraceID: traceID,
SpanID: spanID,
FilteredAttributes: attrs,
}
}
type multierr []error
func (e multierr) errOrNil() error {
if len(e) == 0 {
return nil
} else if len(e) == 1 {
return e[0]
}
return e
}
func (e multierr) Error() string {
es := make([]string, len(e))
for i, err := range e {
es[i] = fmt.Sprintf("* %s", err)
}
return strings.Join(es, "\n\t")
}