promql: aggregations: output directly to matrix for instant queries

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

promql/engine.go

@@ -1313,7 +1313,7 @@ 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)}
+	enh := &EvalNodeHelper{}
 	seriess := make(map[uint64]Series, biggestLen) // Output series by series hash.
 	tempNumSamples := ev.currentSamples
 
@@ -1364,22 +1364,13 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 		enh.Ts = ts
 		result, ws := ev.aggregation(aggExpr, sortedGrouping, param, vector, bufHelper, enh, seriess)
 
-		enh.Out = result[:0] // Reuse result vector.
 		warnings.Merge(ws)
 
 		// If this could be an instant query, shortcut so as not to change sort order.
 		if ev.endTimestamp == ev.startTimestamp {
-			mat := make(Matrix, len(result))
-			for i, s := range result {
-				if s.H == nil {
-					mat[i] = Series{Metric: s.Metric, Floats: []FPoint{{T: ts, F: s.F}}}
-				} else {
-					mat[i] = Series{Metric: s.Metric, Histograms: []HPoint{{T: ts, H: s.H}}}
-				}
-			}
-			ev.currentSamples = originalNumSamples + mat.TotalSamples()
+			ev.currentSamples = originalNumSamples + result.TotalSamples()
 			ev.samplesStats.UpdatePeak(ev.currentSamples)
-			return mat, warnings
+			return result, warnings
 		}
 		if ev.currentSamples > ev.maxSamples {
 			ev.error(ErrTooManySamples(env))
@@ -2735,7 +2726,7 @@ 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, seriess map[uint64]Series) (Vector, annotations.Annotations) {
+func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, param interface{}, vec Vector, seriesHelper []EvalSeriesHelper, enh *EvalNodeHelper, seriess map[uint64]Series) (Matrix, annotations.Annotations) {
 	op := e.Op
 	without := e.Without
 	var annos annotations.Annotations
@@ -2749,7 +2740,7 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, par
 		}
 		k = int64(f)
 		if k < 1 {
-			return Vector{}, annos
+			return nil, annos
 		}
 	}
 	var q float64
@@ -2939,10 +2930,19 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, par
 
 	// Construct the result from the aggregated groups.
 	numSteps := int((ev.endTimestamp-ev.startTimestamp)/ev.interval) + 1
+	var mat Matrix
+	if ev.endTimestamp == ev.startTimestamp {
+		mat = make(Matrix, 0, len(orderedResult))
+	}
+
 	add := func(lbls labels.Labels, f float64, h *histogram.FloatHistogram) {
-		// If this could be an instant query, build a slice so the result is in consistent order.
+		// If this could be an instant query, add directly to the matrix so the result is in consistent order.
 		if ev.endTimestamp == ev.startTimestamp {
-			enh.Out = append(enh.Out, Sample{Metric: lbls, F: f, H: h})
+			if h == nil {
+				mat = append(mat, Series{Metric: lbls, Floats: []FPoint{{T: enh.Ts, F: f}}})
+			} else {
+				mat = append(mat, Series{Metric: lbls, Histograms: []HPoint{{T: enh.Ts, H: h}}})
+			}
 		} else {
 			// Otherwise the results are added into seriess elements.
 			hash := lbls.Hash()
@@ -3029,7 +3029,7 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, grouping []string, par
 		add(aggr.labels, aggr.floatValue, aggr.histogramValue)
 	}
 
-	return enh.Out, annos
+	return mat, annos
 }
 
 // aggregationK evaluates count_values on vec.