promql: refactor: move collection of results into aggregation()

We don't need to check for duplicates as aggregation cannot generate them.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>

promql/engine.go

@@ -1292,7 +1292,6 @@ func (ev *evaluator) rangeEval(prepSeries func(labels.Labels, *EvalSeriesHelper)
 }
 
 func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping []string) (Matrix, annotations.Annotations) {
-	numSteps := int((ev.endTimestamp-ev.startTimestamp)/ev.interval) + 1
 	originalNumSamples := ev.currentSamples
 	var warnings annotations.Annotations
 
@@ -1315,11 +1314,7 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 	var vector Vector // Input vectors for the function.
 	biggestLen := len(inputMatrix)
 	enh := &EvalNodeHelper{Out: make(Vector, 0, biggestLen)}
-	type seriesAndTimestamp struct {
+	seriess := make(map[uint64]Series, biggestLen) // Output series by series hash.
-		Series
-		ts int64
-	}
-	seriess := make(map[uint64]seriesAndTimestamp, biggestLen) // Output series by series hash.
 	tempNumSamples := ev.currentSamples
 
 	// Initialise series helpers with the grouping key.
@@ -1367,7 +1362,7 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 
 		// Make the function call.
 		enh.Ts = ts
-		result, ws := ev.aggregation(aggExpr, sortedGrouping, param, vector, bufHelper, enh)
+		result, ws := ev.aggregation(aggExpr, sortedGrouping, param, vector, bufHelper, enh, seriess)
 
 		enh.Out = result[:0] // Reuse result vector.
 		warnings.Merge(ws)
@@ -1386,9 +1381,6 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 
 		// If this could be an instant query, shortcut so as not to change sort order.
 		if ev.endTimestamp == ev.startTimestamp {
-			if result.ContainsSameLabelset() {
-				ev.errorf("vector cannot contain metrics with the same labelset")
-			}
 			mat := make(Matrix, len(result))
 			for i, s := range result {
 				if s.H == nil {
@@ -1401,32 +1393,6 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 			ev.samplesStats.UpdatePeak(ev.currentSamples)
 			return mat, warnings
 		}
-
-		// Add samples in output vector to output series.
-		for _, sample := range result {
-			h := sample.Metric.Hash()
-			ss, ok := seriess[h]
-			if ok {
-				if ss.ts == ts { // If we've seen this output series before at this timestamp, it's a duplicate.
-					ev.errorf("vector cannot contain metrics with the same labelset")
-				}
-				ss.ts = ts
-			} else {
-				ss = seriesAndTimestamp{Series{Metric: sample.Metric}, ts}
-			}
-			if sample.H == nil {
-				if ss.Floats == nil {
-					ss.Floats = getFPointSlice(numSteps)
-				}
-				ss.Floats = append(ss.Floats, FPoint{T: ts, F: sample.F})
-			} else {
-				if ss.Histograms == nil {
-					ss.Histograms = getHPointSlice(numSteps)
-				}
-				ss.Histograms = append(ss.Histograms, HPoint{T: ts, H: sample.H})
-			}
-			seriess[h] = ss
-		}
 	}
 
 	// Reuse the original point slice.
@@ -1437,7 +1403,7 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 	// Assemble the output matrix. By the time we get here we know we don't have too many samples.
 	mat := make(Matrix, 0, len(seriess))
 	for _, ss := range seriess {
-		mat = append(mat, ss.Series)
+		mat = append(mat, ss)
 	}
 	ev.currentSamples = originalNumSamples + mat.TotalSamples()
 	ev.samplesStats.UpdatePeak(ev.currentSamples)
@@ -2778,7 +2744,7 @@ type groupedAggregation struct {
 
 // aggregation evaluates an aggregation operation on a Vector. The provided grouping labels
 // must be sorted.
-func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, param interface{}, vec Vector, seriesHelper []EvalSeriesHelper, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
+func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, param interface{}, vec Vector, seriesHelper []EvalSeriesHelper, enh *EvalNodeHelper, seriess map[uint64]Series) (Vector, annotations.Annotations) {
 	op := e.Op
 	without := e.Without
 	var annos annotations.Annotations
@@ -3055,7 +3021,36 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, par
 			H:      aggr.histogramValue,
 		})
 	}
-	return enh.Out, annos
+
+	ts := enh.Ts
+	// If this could be an instant query, shortcut so as not to change sort order.
+	if ev.endTimestamp == ev.startTimestamp {
+		return enh.Out, annos
+	}
+
+	numSteps := int((ev.endTimestamp-ev.startTimestamp)/ev.interval) + 1
+	// Add samples in output vector to output series.
+	for _, sample := range enh.Out {
+		h := sample.Metric.Hash()
+		ss, ok := seriess[h]
+		if !ok {
+			ss = Series{Metric: sample.Metric}
+		}
+		if sample.H == nil {
+			if ss.Floats == nil {
+				ss.Floats = getFPointSlice(numSteps)
+			}
+			ss.Floats = append(ss.Floats, FPoint{T: ts, F: sample.F})
+		} else {
+			if ss.Histograms == nil {
+				ss.Histograms = getHPointSlice(numSteps)
+			}
+			ss.Histograms = append(ss.Histograms, HPoint{T: ts, H: sample.H})
+		}
+		seriess[h] = ss
+	}
+
+	return nil, annos
 }
 
 // aggregationK evaluates count_values on vec.