mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
synced 2024-12-26 20:30:10 +01:00
68b6834542
### Describe Your Changes
added opentelemetry exponential histograms support. Such histograms are automatically converted into
VictoriaMetrics histogram with `vmrange` buckets.
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
(cherry picked from commit 9eb0c1fd86)
425 lines
12 KiB
Go
425 lines
12 KiB
Go
package stream
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import (
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"bytes"
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"compress/gzip"
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"fmt"
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"reflect"
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"sort"
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"testing"
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"time"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/fasttime"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/protoparser/opentelemetry/pb"
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)
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func TestParseStream(t *testing.T) {
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f := func(samples []*pb.Metric, tssExpected []prompbmarshal.TimeSeries, usePromNaming bool) {
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t.Helper()
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prevPromNaming := *usePrometheusNaming
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*usePrometheusNaming = usePromNaming
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defer func() {
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*usePrometheusNaming = prevPromNaming
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}()
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checkSeries := func(tss []prompbmarshal.TimeSeries) error {
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if len(tss) != len(tssExpected) {
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return fmt.Errorf("not expected tss count, got: %d, want: %d", len(tss), len(tssExpected))
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}
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sortByMetricName(tss)
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sortByMetricName(tssExpected)
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for i := 0; i < len(tss); i++ {
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ts := tss[i]
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tsExpected := tssExpected[i]
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if len(ts.Labels) != len(tsExpected.Labels) {
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return fmt.Errorf("idx: %d, not expected labels count, got: %d, want: %d", i, len(ts.Labels), len(tsExpected.Labels))
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}
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sortLabels(ts.Labels)
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sortLabels(tsExpected.Labels)
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for j, label := range ts.Labels {
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labelExpected := tsExpected.Labels[j]
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if !reflect.DeepEqual(label, labelExpected) {
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return fmt.Errorf("idx: %d, label idx: %d, not equal label pairs, \ngot: \n%s, \nwant: \n%s",
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i, j, prettifyLabel(label), prettifyLabel(labelExpected))
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}
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}
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if len(ts.Samples) != len(tsExpected.Samples) {
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return fmt.Errorf("idx: %d, not expected samples count, got: %d, want: %d", i, len(ts.Samples), len(tsExpected.Samples))
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}
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for j, sample := range ts.Samples {
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sampleExpected := tsExpected.Samples[j]
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if !reflect.DeepEqual(sample, sampleExpected) {
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return fmt.Errorf("idx: %d, label idx: %d, not equal sample pairs, \ngot: \n%s,\nwant: \n%s",
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i, j, prettifySample(sample), prettifySample(sampleExpected))
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}
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}
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}
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return nil
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}
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req := &pb.ExportMetricsServiceRequest{
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ResourceMetrics: []*pb.ResourceMetrics{
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generateOTLPSamples(samples),
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},
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}
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// Verify protobuf parsing
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pbData := req.MarshalProtobuf(nil)
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if err := checkParseStream(pbData, checkSeries); err != nil {
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t.Fatalf("cannot parse protobuf: %s", err)
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}
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}
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jobLabelValue := prompbmarshal.Label{
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Name: "job",
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Value: "vm",
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}
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leLabel := func(value string) prompbmarshal.Label {
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return prompbmarshal.Label{
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Name: "le",
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Value: value,
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}
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}
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kvLabel := func(k, v string) prompbmarshal.Label {
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return prompbmarshal.Label{
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Name: k,
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Value: v,
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}
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}
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// Test all metric types
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f(
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[]*pb.Metric{
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generateGauge("my-gauge", ""),
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generateHistogram("my-histogram", ""),
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generateSum("my-sum", "", false),
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generateSummary("my-summary", ""),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my-gauge", 15000, 15.0, jobLabelValue, kvLabel("label1", "value1")),
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newPromPBTs("my-histogram_count", 30000, 15.0, jobLabelValue, kvLabel("label2", "value2")),
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newPromPBTs("my-histogram_sum", 30000, 30.0, jobLabelValue, kvLabel("label2", "value2")),
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newPromPBTs("my-histogram_bucket", 30000, 0.0, jobLabelValue, kvLabel("label2", "value2"), leLabel("0.1")),
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newPromPBTs("my-histogram_bucket", 30000, 5.0, jobLabelValue, kvLabel("label2", "value2"), leLabel("0.5")),
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newPromPBTs("my-histogram_bucket", 30000, 15.0, jobLabelValue, kvLabel("label2", "value2"), leLabel("1")),
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newPromPBTs("my-histogram_bucket", 30000, 15.0, jobLabelValue, kvLabel("label2", "value2"), leLabel("5")),
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newPromPBTs("my-histogram_bucket", 30000, 15.0, jobLabelValue, kvLabel("label2", "value2"), leLabel("+Inf")),
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newPromPBTs("my-sum", 150000, 15.5, jobLabelValue, kvLabel("label5", "value5")),
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newPromPBTs("my-summary_sum", 35000, 32.5, jobLabelValue, kvLabel("label6", "value6")),
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newPromPBTs("my-summary_count", 35000, 5.0, jobLabelValue, kvLabel("label6", "value6")),
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newPromPBTs("my-summary", 35000, 7.5, jobLabelValue, kvLabel("label6", "value6"), kvLabel("quantile", "0.1")),
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newPromPBTs("my-summary", 35000, 10.0, jobLabelValue, kvLabel("label6", "value6"), kvLabel("quantile", "0.5")),
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newPromPBTs("my-summary", 35000, 15.0, jobLabelValue, kvLabel("label6", "value6"), kvLabel("quantile", "1")),
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},
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false,
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)
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// Test gauge
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f(
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[]*pb.Metric{
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generateGauge("my-gauge", ""),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my-gauge", 15000, 15.0, jobLabelValue, kvLabel("label1", "value1")),
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},
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false,
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)
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// Test gauge with unit and prometheus naming
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f(
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[]*pb.Metric{
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generateGauge("my-gauge", "ms"),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_gauge_milliseconds", 15000, 15.0, jobLabelValue, kvLabel("label1", "value1")),
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},
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true,
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)
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// Test gauge with unit inside metric
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f(
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[]*pb.Metric{
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generateGauge("my-gauge-milliseconds", "ms"),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_gauge_milliseconds", 15000, 15.0, jobLabelValue, kvLabel("label1", "value1")),
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},
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true,
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)
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// Test gauge with ratio suffix
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f(
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[]*pb.Metric{
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generateGauge("my-gauge-milliseconds", "1"),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_gauge_milliseconds_ratio", 15000, 15.0, jobLabelValue, kvLabel("label1", "value1")),
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},
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true,
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)
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// Test sum with total suffix
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f(
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[]*pb.Metric{
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generateSum("my-sum", "ms", true),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_sum_milliseconds_total", 150000, 15.5, jobLabelValue, kvLabel("label5", "value5")),
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},
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true,
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)
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// Test sum with total suffix, which exists in a metric name
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f(
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[]*pb.Metric{
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generateSum("my-total-sum", "ms", true),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_sum_milliseconds_total", 150000, 15.5, jobLabelValue, kvLabel("label5", "value5")),
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},
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true,
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)
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// Test sum with total and complex suffix
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f(
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[]*pb.Metric{
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generateSum("my-total-sum", "m/s", true),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("my_sum_meters_per_second_total", 150000, 15.5, jobLabelValue, kvLabel("label5", "value5")),
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},
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true,
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)
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// Test exponential histograms
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f(
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[]*pb.Metric{
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generateExpHistogram("test-histogram", "m/s"),
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},
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[]prompbmarshal.TimeSeries{
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newPromPBTs("test_histogram_meters_per_second_bucket", 15000, 5.0, jobLabelValue, kvLabel("label1", "value1"), kvLabel("vmrange", "1.061e+00...1.067e+00")),
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newPromPBTs("test_histogram_meters_per_second_bucket", 15000, 10.0, jobLabelValue, kvLabel("label1", "value1"), kvLabel("vmrange", "1.067e+00...1.073e+00")),
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newPromPBTs("test_histogram_meters_per_second_bucket", 15000, 1.0, jobLabelValue, kvLabel("label1", "value1"), kvLabel("vmrange", "1.085e+00...1.091e+00")),
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newPromPBTs("test_histogram_meters_per_second_count", 15000, 20.0, jobLabelValue, kvLabel("label1", "value1")),
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newPromPBTs("test_histogram_meters_per_second_sum", 15000, 4578.0, jobLabelValue, kvLabel("label1", "value1")),
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},
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true,
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)
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}
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func checkParseStream(data []byte, checkSeries func(tss []prompbmarshal.TimeSeries) error) error {
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// Verify parsing without compression
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if err := ParseStream(bytes.NewBuffer(data), false, nil, checkSeries); err != nil {
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return fmt.Errorf("error when parsing data: %w", err)
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}
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// Verify parsing with compression
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var bb bytes.Buffer
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zw := gzip.NewWriter(&bb)
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if _, err := zw.Write(data); err != nil {
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return fmt.Errorf("cannot compress data: %w", err)
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}
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if err := zw.Close(); err != nil {
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return fmt.Errorf("cannot close gzip writer: %w", err)
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}
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if err := ParseStream(&bb, true, nil, checkSeries); err != nil {
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return fmt.Errorf("error when parsing compressed data: %w", err)
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}
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return nil
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}
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func attributesFromKV(k, v string) []*pb.KeyValue {
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return []*pb.KeyValue{
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{
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Key: k,
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Value: &pb.AnyValue{
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StringValue: &v,
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},
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},
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}
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}
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func generateExpHistogram(name, unit string) *pb.Metric {
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sum := float64(4578)
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return &pb.Metric{
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Name: name,
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Unit: unit,
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ExponentialHistogram: &pb.ExponentialHistogram{
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AggregationTemporality: pb.AggregationTemporalityCumulative,
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DataPoints: []*pb.ExponentialHistogramDataPoint{
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{
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Attributes: attributesFromKV("label1", "value1"),
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TimeUnixNano: uint64(15 * time.Second),
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Count: 20,
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Sum: &sum,
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Scale: 7,
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Positive: &pb.Buckets{
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Offset: 7,
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BucketCounts: []uint64{0, 0, 0, 0, 5, 10, 0, 0, 1},
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},
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},
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},
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},
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}
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}
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func generateGauge(name, unit string) *pb.Metric {
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n := int64(15)
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points := []*pb.NumberDataPoint{
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{
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Attributes: attributesFromKV("label1", "value1"),
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IntValue: &n,
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TimeUnixNano: uint64(15 * time.Second),
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},
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}
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return &pb.Metric{
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Name: name,
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Unit: unit,
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Gauge: &pb.Gauge{
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DataPoints: points,
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},
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}
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}
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func generateHistogram(name, unit string) *pb.Metric {
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points := []*pb.HistogramDataPoint{
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{
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Attributes: attributesFromKV("label2", "value2"),
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Count: 15,
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Sum: func() *float64 { v := 30.0; return &v }(),
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ExplicitBounds: []float64{0.1, 0.5, 1.0, 5.0},
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BucketCounts: []uint64{0, 5, 10, 0, 0},
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TimeUnixNano: uint64(30 * time.Second),
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},
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}
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return &pb.Metric{
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Name: name,
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Unit: unit,
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Histogram: &pb.Histogram{
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AggregationTemporality: pb.AggregationTemporalityCumulative,
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DataPoints: points,
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},
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}
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}
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func generateSum(name, unit string, isMonotonic bool) *pb.Metric {
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d := float64(15.5)
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points := []*pb.NumberDataPoint{
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{
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Attributes: attributesFromKV("label5", "value5"),
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DoubleValue: &d,
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TimeUnixNano: uint64(150 * time.Second),
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},
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}
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return &pb.Metric{
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Name: name,
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Unit: unit,
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Sum: &pb.Sum{
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AggregationTemporality: pb.AggregationTemporalityCumulative,
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DataPoints: points,
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IsMonotonic: isMonotonic,
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},
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}
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}
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func generateSummary(name, unit string) *pb.Metric {
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points := []*pb.SummaryDataPoint{
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{
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Attributes: attributesFromKV("label6", "value6"),
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TimeUnixNano: uint64(35 * time.Second),
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Sum: 32.5,
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Count: 5,
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QuantileValues: []*pb.ValueAtQuantile{
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{
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Quantile: 0.1,
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Value: 7.5,
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},
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{
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Quantile: 0.5,
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Value: 10.0,
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},
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{
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Quantile: 1.0,
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Value: 15.0,
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},
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},
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},
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}
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return &pb.Metric{
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Name: name,
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Unit: unit,
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Summary: &pb.Summary{
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DataPoints: points,
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},
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}
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}
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func generateOTLPSamples(srcs []*pb.Metric) *pb.ResourceMetrics {
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otlpMetrics := &pb.ResourceMetrics{
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Resource: &pb.Resource{
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Attributes: attributesFromKV("job", "vm"),
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},
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}
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otlpMetrics.ScopeMetrics = []*pb.ScopeMetrics{
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{
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Metrics: append([]*pb.Metric{}, srcs...),
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},
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}
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return otlpMetrics
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}
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func newPromPBTs(metricName string, t int64, v float64, extraLabels ...prompbmarshal.Label) prompbmarshal.TimeSeries {
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if t <= 0 {
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// Set the current timestamp if t isn't set.
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t = int64(fasttime.UnixTimestamp()) * 1000
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}
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ts := prompbmarshal.TimeSeries{
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Labels: []prompbmarshal.Label{
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{
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Name: "__name__",
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Value: metricName,
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},
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},
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Samples: []prompbmarshal.Sample{
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{
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Value: v,
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Timestamp: t,
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},
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},
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}
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ts.Labels = append(ts.Labels, extraLabels...)
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return ts
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}
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func prettifyLabel(label prompbmarshal.Label) string {
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return fmt.Sprintf("name=%q value=%q", label.Name, label.Value)
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}
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func prettifySample(sample prompbmarshal.Sample) string {
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return fmt.Sprintf("sample=%f timestamp: %d", sample.Value, sample.Timestamp)
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}
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func sortByMetricName(tss []prompbmarshal.TimeSeries) {
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sort.Slice(tss, func(i, j int) bool {
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return getMetricName(tss[i].Labels) < getMetricName(tss[j].Labels)
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})
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}
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func getMetricName(labels []prompbmarshal.Label) string {
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for _, l := range labels {
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if l.Name == "__name__" {
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return l.Value
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}
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}
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return ""
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}
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func sortLabels(labels []prompbmarshal.Label) {
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sort.Slice(labels, func(i, j int) bool {
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return labels[i].Name < labels[j].Name
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})
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}
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