[FEATURE] PromQL: Add experimental info function MVP (#14495)
The `info` function is an experiment to improve UX around including labels from info metrics. `info` has to be enabled via the feature flag `--enable-feature=promql-experimental-functions`. This MVP of info simplifies the implementation by assuming: * Only support for the target_info metric * That target_info's identifying labels are job and instance Also: * Encode info samples' original timestamp as sample value * Deduce info series select hints from top-most VectorSelector --------- Signed-off-by: Arve Knudsen <arve.knudsen@gmail.com> Co-authored-by: Ying WANG <ying.wang@grafana.com> Co-authored-by: Augustin Husson <augustin.husson@amadeus.com> Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com> Co-authored-by: Björn Rabenstein <github@rabenste.in> Co-authored-by: Bryan Boreham <bjboreham@gmail.com>pull/15007/head
Arve Knudsen
2 years ago
committed by
GitHub
parent
249969d7ff
commit
de16f5e387
@ -0,0 +1,454 @@ |
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// Copyright 2024 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package promql |
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import ( |
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"context" |
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"errors" |
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"fmt" |
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"slices" |
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"strings" |
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"github.com/grafana/regexp" |
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"github.com/prometheus/prometheus/model/labels" |
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"github.com/prometheus/prometheus/promql/parser" |
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"github.com/prometheus/prometheus/storage" |
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"github.com/prometheus/prometheus/util/annotations" |
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) |
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const targetInfo = "target_info" |
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// identifyingLabels are the labels we consider as identifying for info metrics.
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// Currently hard coded, so we don't need knowledge of individual info metrics.
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var identifyingLabels = []string{"instance", "job"} |
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// evalInfo implements the info PromQL function.
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func (ev *evaluator) evalInfo(ctx context.Context, args parser.Expressions) (parser.Value, annotations.Annotations) { |
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val, annots := ev.eval(ctx, args[0]) |
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mat := val.(Matrix) |
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// Map from data label name to matchers.
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dataLabelMatchers := map[string][]*labels.Matcher{} |
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var infoNameMatchers []*labels.Matcher |
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if len(args) > 1 { |
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// TODO: Introduce a dedicated LabelSelector type.
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labelSelector := args[1].(*parser.VectorSelector) |
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for _, m := range labelSelector.LabelMatchers { |
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dataLabelMatchers[m.Name] = append(dataLabelMatchers[m.Name], m) |
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if m.Name == labels.MetricName { |
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infoNameMatchers = append(infoNameMatchers, m) |
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} |
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} |
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} else { |
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infoNameMatchers = []*labels.Matcher{labels.MustNewMatcher(labels.MatchEqual, labels.MetricName, targetInfo)} |
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} |
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// Don't try to enrich info series.
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ignoreSeries := map[int]struct{}{} |
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loop: |
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for i, s := range mat { |
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name := s.Metric.Get(labels.MetricName) |
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for _, m := range infoNameMatchers { |
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if m.Matches(name) { |
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ignoreSeries[i] = struct{}{} |
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continue loop |
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} |
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} |
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} |
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selectHints := ev.infoSelectHints(args[0]) |
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infoSeries, ws, err := ev.fetchInfoSeries(ctx, mat, ignoreSeries, dataLabelMatchers, selectHints) |
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if err != nil { |
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ev.error(err) |
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} |
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annots.Merge(ws) |
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res, ws := ev.combineWithInfoSeries(ctx, mat, infoSeries, ignoreSeries, dataLabelMatchers) |
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annots.Merge(ws) |
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return res, annots |
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} |
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// infoSelectHints calculates the storage.SelectHints for selecting info series, given expr (first argument to info call).
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func (ev *evaluator) infoSelectHints(expr parser.Expr) storage.SelectHints { |
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var nodeTimestamp *int64 |
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var offset int64 |
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parser.Inspect(expr, func(node parser.Node, path []parser.Node) error { |
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switch n := node.(type) { |
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case *parser.VectorSelector: |
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if n.Timestamp != nil { |
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nodeTimestamp = n.Timestamp |
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} |
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offset = durationMilliseconds(n.OriginalOffset) |
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return fmt.Errorf("end traversal") |
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default: |
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return nil |
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} |
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}) |
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start := ev.startTimestamp |
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end := ev.endTimestamp |
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if nodeTimestamp != nil { |
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// The timestamp on the selector overrides everything.
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start = *nodeTimestamp |
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end = *nodeTimestamp |
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} |
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// Reduce the start by one fewer ms than the lookback delta
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// because wo want to exclude samples that are precisely the
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// lookback delta before the eval time.
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start -= durationMilliseconds(ev.lookbackDelta) - 1 |
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start -= offset |
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end -= offset |
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return storage.SelectHints{ |
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Start: start, |
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End: end, |
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Step: ev.interval, |
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Func: "info", |
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} |
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} |
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// fetchInfoSeries fetches info series given matching identifying labels in mat.
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// Series in ignoreSeries are not fetched.
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// dataLabelMatchers may be mutated.
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func (ev *evaluator) fetchInfoSeries(ctx context.Context, mat Matrix, ignoreSeries map[int]struct{}, dataLabelMatchers map[string][]*labels.Matcher, selectHints storage.SelectHints) (Matrix, annotations.Annotations, error) { |
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// A map of values for all identifying labels we are interested in.
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idLblValues := map[string]map[string]struct{}{} |
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for i, s := range mat { |
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if _, exists := ignoreSeries[i]; exists { |
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continue |
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} |
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// Register relevant values per identifying label for this series.
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for _, l := range identifyingLabels { |
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val := s.Metric.Get(l) |
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if val == "" { |
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continue |
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} |
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if idLblValues[l] == nil { |
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idLblValues[l] = map[string]struct{}{} |
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} |
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idLblValues[l][val] = struct{}{} |
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} |
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} |
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if len(idLblValues) == 0 { |
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return nil, nil, nil |
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} |
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// Generate regexps for every interesting value per identifying label.
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var sb strings.Builder |
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idLblRegexps := make(map[string]string, len(idLblValues)) |
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for name, vals := range idLblValues { |
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sb.Reset() |
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i := 0 |
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for v := range vals { |
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if i > 0 { |
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sb.WriteRune('|') |
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} |
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sb.WriteString(regexp.QuoteMeta(v)) |
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i++ |
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} |
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idLblRegexps[name] = sb.String() |
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} |
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var infoLabelMatchers []*labels.Matcher |
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for name, re := range idLblRegexps { |
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infoLabelMatchers = append(infoLabelMatchers, labels.MustNewMatcher(labels.MatchRegexp, name, re)) |
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} |
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var nameMatcher *labels.Matcher |
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for name, ms := range dataLabelMatchers { |
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for i, m := range ms { |
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if m.Name == labels.MetricName { |
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nameMatcher = m |
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ms = slices.Delete(ms, i, i+1) |
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} |
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infoLabelMatchers = append(infoLabelMatchers, m) |
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} |
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if len(ms) > 0 { |
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dataLabelMatchers[name] = ms |
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} else { |
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delete(dataLabelMatchers, name) |
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} |
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} |
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if nameMatcher == nil { |
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// Default to using the target_info metric.
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infoLabelMatchers = append([]*labels.Matcher{labels.MustNewMatcher(labels.MatchEqual, labels.MetricName, targetInfo)}, infoLabelMatchers...) |
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} |
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infoIt := ev.querier.Select(ctx, false, &selectHints, infoLabelMatchers...) |
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infoSeries, ws, err := expandSeriesSet(ctx, infoIt) |
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if err != nil { |
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return nil, ws, err |
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} |
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infoMat := ev.evalSeries(ctx, infoSeries, 0, true) |
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return infoMat, ws, nil |
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} |
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// combineWithInfoSeries combines mat with select data labels from infoMat.
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func (ev *evaluator) combineWithInfoSeries(ctx context.Context, mat, infoMat Matrix, ignoreSeries map[int]struct{}, dataLabelMatchers map[string][]*labels.Matcher) (Matrix, annotations.Annotations) { |
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buf := make([]byte, 0, 1024) |
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lb := labels.NewScratchBuilder(0) |
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sigFunction := func(name string) func(labels.Labels) string { |
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return func(lset labels.Labels) string { |
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lb.Reset() |
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lb.Add(labels.MetricName, name) |
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lset.MatchLabels(true, identifyingLabels...).Range(func(l labels.Label) { |
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lb.Add(l.Name, l.Value) |
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}) |
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lb.Sort() |
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return string(lb.Labels().Bytes(buf)) |
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} |
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} |
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infoMetrics := map[string]struct{}{} |
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for _, is := range infoMat { |
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lblMap := is.Metric.Map() |
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infoMetrics[lblMap[labels.MetricName]] = struct{}{} |
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} |
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sigfs := make(map[string]func(labels.Labels) string, len(infoMetrics)) |
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for name := range infoMetrics { |
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sigfs[name] = sigFunction(name) |
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} |
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// Keep a copy of the original point slices so they can be returned to the pool.
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origMatrices := []Matrix{ |
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make(Matrix, len(mat)), |
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make(Matrix, len(infoMat)), |
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} |
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copy(origMatrices[0], mat) |
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copy(origMatrices[1], infoMat) |
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numSteps := int((ev.endTimestamp-ev.startTimestamp)/ev.interval) + 1 |
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originalNumSamples := ev.currentSamples |
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// Create an output vector that is as big as the input matrix with
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// the most time series.
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biggestLen := max(len(mat), len(infoMat)) |
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baseVector := make(Vector, 0, len(mat)) |
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infoVector := make(Vector, 0, len(infoMat)) |
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enh := &EvalNodeHelper{ |
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Out: make(Vector, 0, biggestLen), |
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} |
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type seriesAndTimestamp struct { |
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Series |
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ts int64 |
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} |
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seriess := make(map[uint64]seriesAndTimestamp, biggestLen) // Output series by series hash.
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tempNumSamples := ev.currentSamples |
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// For every base series, compute signature per info metric.
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baseSigs := make([]map[string]string, 0, len(mat)) |
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for _, s := range mat { |
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sigs := make(map[string]string, len(infoMetrics)) |
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for infoName := range infoMetrics { |
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sigs[infoName] = sigfs[infoName](s.Metric) |
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} |
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baseSigs = append(baseSigs, sigs) |
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} |
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infoSigs := make([]string, 0, len(infoMat)) |
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for _, s := range infoMat { |
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name := s.Metric.Map()[labels.MetricName] |
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infoSigs = append(infoSigs, sigfs[name](s.Metric)) |
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} |
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var warnings annotations.Annotations |
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for ts := ev.startTimestamp; ts <= ev.endTimestamp; ts += ev.interval { |
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if err := contextDone(ctx, "expression evaluation"); err != nil { |
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ev.error(err) |
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} |
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// Reset number of samples in memory after each timestamp.
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ev.currentSamples = tempNumSamples |
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// Gather input vectors for this timestamp.
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baseVector, _ = ev.gatherVector(ts, mat, baseVector, nil, nil) |
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infoVector, _ = ev.gatherVector(ts, infoMat, infoVector, nil, nil) |
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enh.Ts = ts |
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result, err := ev.combineWithInfoVector(baseVector, infoVector, ignoreSeries, baseSigs, infoSigs, enh, dataLabelMatchers) |
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if err != nil { |
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ev.error(err) |
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} |
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enh.Out = result[:0] // Reuse result vector.
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vecNumSamples := result.TotalSamples() |
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ev.currentSamples += vecNumSamples |
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// When we reset currentSamples to tempNumSamples during the next iteration of the loop it also
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// needs to include the samples from the result here, as they're still in memory.
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tempNumSamples += vecNumSamples |
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ev.samplesStats.UpdatePeak(ev.currentSamples) |
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if ev.currentSamples > ev.maxSamples { |
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ev.error(ErrTooManySamples(env)) |
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} |
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// Add samples in result vector to output series.
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for _, sample := range result { |
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h := sample.Metric.Hash() |
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ss, exists := seriess[h] |
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if exists { |
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if ss.ts == ts { // If we've seen this output series before at this timestamp, it's a duplicate.
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ev.errorf("vector cannot contain metrics with the same labelset") |
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} |
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ss.ts = ts |
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} else { |
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ss = seriesAndTimestamp{Series{Metric: sample.Metric}, ts} |
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} |
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addToSeries(&ss.Series, enh.Ts, sample.F, sample.H, numSteps) |
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seriess[h] = ss |
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} |
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} |
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// Reuse the original point slices.
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for _, m := range origMatrices { |
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for _, s := range m { |
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putFPointSlice(s.Floats) |
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putHPointSlice(s.Histograms) |
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} |
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} |
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// Assemble the output matrix. By the time we get here we know we don't have too many samples.
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numSamples := 0 |
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output := make(Matrix, 0, len(seriess)) |
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for _, ss := range seriess { |
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numSamples += len(ss.Floats) + totalHPointSize(ss.Histograms) |
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output = append(output, ss.Series) |
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} |
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ev.currentSamples = originalNumSamples + numSamples |
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ev.samplesStats.UpdatePeak(ev.currentSamples) |
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return output, warnings |
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} |
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// combineWithInfoVector combines base and info Vectors.
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// Base series in ignoreSeries are not combined.
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func (ev *evaluator) combineWithInfoVector(base, info Vector, ignoreSeries map[int]struct{}, baseSigs []map[string]string, infoSigs []string, enh *EvalNodeHelper, dataLabelMatchers map[string][]*labels.Matcher) (Vector, error) { |
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if len(base) == 0 { |
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return nil, nil // Short-circuit: nothing is going to match.
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} |
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// All samples from the info Vector hashed by the matching label/values.
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if enh.rightSigs == nil { |
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enh.rightSigs = make(map[string]Sample, len(enh.Out)) |
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} else { |
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clear(enh.rightSigs) |
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} |
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for i, s := range info { |
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if s.H != nil { |
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ev.error(errors.New("info sample should be float")) |
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} |
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// We encode original info sample timestamps via the float value.
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origT := int64(s.F) |
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sig := infoSigs[i] |
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if existing, exists := enh.rightSigs[sig]; exists { |
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// We encode original info sample timestamps via the float value.
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existingOrigT := int64(existing.F) |
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switch { |
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case existingOrigT > origT: |
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// Keep the other info sample, since it's newer.
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case existingOrigT < origT: |
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// Keep this info sample, since it's newer.
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enh.rightSigs[sig] = s |
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default: |
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// The two info samples have the same timestamp - conflict.
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name := s.Metric.Map()[labels.MetricName] |
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ev.errorf("found duplicate series for info metric %s", name) |
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} |
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} else { |
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enh.rightSigs[sig] = s |
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} |
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} |
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for i, bs := range base { |
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if _, exists := ignoreSeries[i]; exists { |
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// This series should not be enriched with info metric data labels.
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enh.Out = append(enh.Out, Sample{ |
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Metric: bs.Metric, |
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F: bs.F, |
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H: bs.H, |
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}) |
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continue |
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} |
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baseLabels := bs.Metric.Map() |
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enh.resetBuilder(labels.Labels{}) |
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// For every info metric name, try to find an info series with the same signature.
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seenInfoMetrics := map[string]struct{}{} |
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for infoName, sig := range baseSigs[i] { |
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is, exists := enh.rightSigs[sig] |
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if !exists { |
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continue |
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} |
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if _, exists := seenInfoMetrics[infoName]; exists { |
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continue |
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} |
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err := is.Metric.Validate(func(l labels.Label) error { |
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if l.Name == labels.MetricName { |
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return nil |
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} |
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if _, exists := dataLabelMatchers[l.Name]; len(dataLabelMatchers) > 0 && !exists { |
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// Not among the specified data label matchers.
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return nil |
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} |
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if v := enh.lb.Get(l.Name); v != "" && v != l.Value { |
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return fmt.Errorf("conflicting label: %s", l.Name) |
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} |
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if _, exists := baseLabels[l.Name]; exists { |
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// Skip labels already on the base metric.
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return nil |
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} |
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enh.lb.Set(l.Name, l.Value) |
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return nil |
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}) |
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if err != nil { |
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return nil, err |
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} |
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seenInfoMetrics[infoName] = struct{}{} |
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} |
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infoLbls := enh.lb.Labels() |
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if infoLbls.Len() == 0 { |
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// If there's at least one data label matcher not matching the empty string,
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// we have to ignore this series as there are no matching info series.
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allMatchersMatchEmpty := true |
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for _, ms := range dataLabelMatchers { |
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for _, m := range ms { |
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if !m.Matches("") { |
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allMatchersMatchEmpty = false |
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break |
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} |
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} |
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} |
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if !allMatchersMatchEmpty { |
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continue |
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} |
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} |
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enh.resetBuilder(bs.Metric) |
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infoLbls.Range(func(l labels.Label) { |
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enh.lb.Set(l.Name, l.Value) |
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}) |
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enh.Out = append(enh.Out, Sample{ |
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Metric: enh.lb.Labels(), |
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F: bs.F, |
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H: bs.H, |
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}) |
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} |
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return enh.Out, nil |
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} |
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@ -0,0 +1,140 @@ |
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// Copyright 2024 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package promql_test |
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import ( |
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"testing" |
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"github.com/prometheus/prometheus/promql/promqltest" |
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) |
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// The "info" function is experimental. This is why we write those tests here for now instead of promqltest/testdata/info.test.
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func TestInfo(t *testing.T) { |
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engine := promqltest.NewTestEngine(t, false, 0, promqltest.DefaultMaxSamplesPerQuery) |
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promqltest.RunTest(t, ` |
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load 5m |
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metric{instance="a", job="1", label="value"} 0 1 2 |
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metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2 |
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metric_with_overlapping_label{instance="a", job="1", label="value", data="base"} 0 1 2 |
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target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 1 |
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build_info{instance="a", job="1", build_data="build"} 1 1 1 |
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# Include one info metric data label. |
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eval range from 0m to 10m step 5m info(metric, {data=~".+"}) |
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metric{data="info", instance="a", job="1", label="value"} 0 1 2 |
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# Include all info metric data labels. |
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eval range from 0m to 10m step 5m info(metric) |
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metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2 |
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# Try including all info metric data labels, but non-matching identifying labels. |
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eval range from 0m to 10m step 5m info(metric_not_matching_target_info) |
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metric_not_matching_target_info{instance="a", job="2", label="value"} 0 1 2 |
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|
||||
# Try including a certain info metric data label with a non-matching matcher not accepting empty labels. |
||||
# Metric is ignored, due there being a data label matcher not matching empty labels, |
||||
# and there being no info series matches. |
||||
eval range from 0m to 10m step 5m info(metric, {non_existent=~".+"}) |
||||
|
||||
# Include a certain info metric data label together with a non-matching matcher accepting empty labels. |
||||
# Since the non_existent matcher matches empty labels, it's simply ignored when there's no match. |
||||
# XXX: This case has to include a matcher not matching empty labels, due the PromQL limitation |
||||
# that vector selectors have to contain at least one matcher not accepting empty labels. |
||||
# We might need another construct than vector selector to get around this limitation. |
||||
eval range from 0m to 10m step 5m info(metric, {data=~".+", non_existent=~".*"}) |
||||
metric{data="info", instance="a", job="1", label="value"} 0 1 2 |
||||
|
||||
# Info series data labels overlapping with those of base series are ignored. |
||||
eval range from 0m to 10m step 5m info(metric_with_overlapping_label) |
||||
metric_with_overlapping_label{data="base", instance="a", job="1", label="value", another_data="another info"} 0 1 2 |
||||
|
||||
# Include data labels from target_info specifically. |
||||
eval range from 0m to 10m step 5m info(metric, {__name__="target_info"}) |
||||
metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 2 |
||||
|
||||
# Try to include all data labels from a non-existent info metric. |
||||
eval range from 0m to 10m step 5m info(metric, {__name__="non_existent"}) |
||||
metric{instance="a", job="1", label="value"} 0 1 2 |
||||
|
||||
# Try to include a certain data label from a non-existent info metric. |
||||
eval range from 0m to 10m step 5m info(metric, {__name__="non_existent", data=~".+"}) |
||||
|
||||
# Include data labels from build_info. |
||||
eval range from 0m to 10m step 5m info(metric, {__name__="build_info"}) |
||||
metric{instance="a", job="1", label="value", build_data="build"} 0 1 2 |
||||
|
||||
# Include data labels from build_info and target_info. |
||||
eval range from 0m to 10m step 5m info(metric, {__name__=~".+_info"}) |
||||
metric{instance="a", job="1", label="value", build_data="build", data="info", another_data="another info"} 0 1 2 |
||||
|
||||
# Info metrics themselves are ignored when it comes to enriching with info metric data labels. |
||||
eval range from 0m to 10m step 5m info(build_info, {__name__=~".+_info", build_data=~".+"}) |
||||
build_info{instance="a", job="1", build_data="build"} 1 1 1 |
||||
|
||||
clear |
||||
|
||||
# Overlapping target_info series. |
||||
load 5m |
||||
metric{instance="a", job="1", label="value"} 0 1 2 |
||||
target_info{instance="a", job="1", data="info", another_data="another info"} 1 1 _ |
||||
target_info{instance="a", job="1", data="updated info", another_data="another info"} _ _ 1 |
||||
|
||||
# Conflicting info series are resolved through picking the latest sample. |
||||
eval range from 0m to 10m step 5m info(metric) |
||||
metric{data="info", instance="a", job="1", label="value", another_data="another info"} 0 1 _ |
||||
metric{data="updated info", instance="a", job="1", label="value", another_data="another info"} _ _ 2 |
||||
|
||||
clear |
||||
|
||||
# Non-overlapping target_info series. |
||||
load 5m |
||||
metric{instance="a", job="1", label="value"} 0 1 2 |
||||
target_info{instance="a", job="1", data="info"} 1 1 stale |
||||
target_info{instance="a", job="1", data="updated info"} _ _ 1 |
||||
|
||||
# Include info metric data labels from a metric which data labels change over time. |
||||
eval range from 0m to 10m step 5m info(metric) |
||||
metric{data="info", instance="a", job="1", label="value"} 0 1 _ |
||||
metric{data="updated info", instance="a", job="1", label="value"} _ _ 2 |
||||
|
||||
clear |
||||
|
||||
# Info series selector matches histogram series, info metrics should be float type. |
||||
load 5m |
||||
metric{instance="a", job="1", label="value"} 0 1 2 |
||||
histogram{instance="a", job="1"} {{schema:1 sum:3 count:22 buckets:[5 10 7]}} |
||||
|
||||
eval_fail range from 0m to 10m step 5m info(metric, {__name__="histogram"}) |
||||
|
||||
clear |
||||
|
||||
# Series with skipped scrape. |
||||
load 1m |
||||
metric{instance="a", job="1", label="value"} 0 _ 2 3 4 |
||||
target_info{instance="a", job="1", data="info"} 1 _ 1 1 1 |
||||
|
||||
# Lookback works also for the info series. |
||||
eval range from 1m to 4m step 1m info(metric) |
||||
metric{data="info", instance="a", job="1", label="value"} 0 2 3 4 |
||||
|
||||
# @ operator works also with info. |
||||
# Note that we pick the timestamp missing a sample, lookback should pick previous sample. |
||||
eval range from 1m to 4m step 1m info(metric @ 60) |
||||
metric{data="info", instance="a", job="1", label="value"} 0 0 0 0 |
||||
|
||||
# offset operator works also with info. |
||||
eval range from 1m to 4m step 1m info(metric offset 1m) |
||||
metric{data="info", instance="a", job="1", label="value"} 0 0 2 3 |
||||
`, engine) |
||||
} |
||||
Loading…
Reference in new issue