Cleanup PromQL functions (#6551)

* Cleanup PromQL functions

The engine ensures, for Matrix functions, that functions are called with exactly one series at the time.
Therefore a lot of code can be inlined and we can directly assume the first element of the arguments exists and contains all the samples needed.

Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu>

promql/functions.go

@@ -66,16 +66,15 @@ func extrapolatedRate(vals []Value, args Expressions, enh *EvalNodeHelper, isCou
 	ms := args[0].(*MatrixSelector)
 
 	var (
-		matrix     = vals[0].(Matrix)
+		samples    = vals[0].(Matrix)[0]
 		rangeStart = enh.ts - durationMilliseconds(ms.Range+ms.Offset)
 		rangeEnd   = enh.ts - durationMilliseconds(ms.Offset)
 	)
 
-	for _, samples := range matrix {
 	// No sense in trying to compute a rate without at least two points. Drop
 	// this Vector element.
 	if len(samples.Points) < 2 {
-			continue
+		return enh.out
 	}
 	var (
 		counterCorrection float64
@@ -132,12 +131,10 @@ func extrapolatedRate(vals []Value, args Expressions, enh *EvalNodeHelper, isCou
 		resultValue = resultValue / ms.Range.Seconds()
 	}
 
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: resultValue},
 	})
 }
-	return enh.out
-}
 
 // === delta(Matrix ValueTypeMatrix) Vector ===
 func funcDelta(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
@@ -165,11 +162,11 @@ func funcIdelta(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 }
 
 func instantValue(vals []Value, out Vector, isRate bool) Vector {
-	for _, samples := range vals[0].(Matrix) {
+	samples := vals[0].(Matrix)[0]
 	// No sense in trying to compute a rate without at least two points. Drop
 	// this Vector element.
 	if len(samples.Points) < 2 {
-			continue
+		return out
 	}
 
 	lastSample := samples.Points[len(samples.Points)-1]
@@ -186,7 +183,7 @@ func instantValue(vals []Value, out Vector, isRate bool) Vector {
 	sampledInterval := lastSample.T - previousSample.T
 	if sampledInterval == 0 {
 		// Avoid dividing by 0.
-			continue
+		return out
 	}
 
 	if isRate {
@@ -194,12 +191,10 @@ func instantValue(vals []Value, out Vector, isRate bool) Vector {
 		resultValue /= float64(sampledInterval) / 1000
 	}
 
-		out = append(out, Sample{
+	return append(out, Sample{
 		Point: Point{V: resultValue},
 	})
 }
-	return out
-}
 
 // Calculate the trend value at the given index i in raw data d.
 // This is somewhat analogous to the slope of the trend at the given index.
@@ -223,7 +218,7 @@ func calcTrendValue(i int, sf, tf, s0, s1, b float64) float64 {
 // how trends in historical data will affect the current data. A higher trend factor increases the influence.
 // of trends. Algorithm taken from https://en.wikipedia.org/wiki/Exponential_smoothing titled: "Double exponential smoothing".
 func funcHoltWinters(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
-	mat := vals[0].(Matrix)
+	samples := vals[0].(Matrix)[0]
 
 	// The smoothing factor argument.
 	sf := vals[1].(Vector)[0].V
@@ -239,13 +234,11 @@ func funcHoltWinters(vals []Value, args Expressions, enh *EvalNodeHelper) Vector
 		panic(errors.Errorf("invalid trend factor. Expected: 0 < tf < 1, got: %f", tf))
 	}
 
-	var l int
+	l := len(samples.Points)
-	for _, samples := range mat {
-		l = len(samples.Points)
 
 	// Can't do the smoothing operation with less than two points.
 	if l < 2 {
-			continue
+		return enh.out
 	}
 
 	var s0, s1, b float64
@@ -267,14 +260,11 @@ func funcHoltWinters(vals []Value, args Expressions, enh *EvalNodeHelper) Vector
 		s0, s1 = s1, x+y
 	}
 
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: s1},
 	})
 }
 
-	return enh.out
-}
-
 // === sort(node ValueTypeVector) Vector ===
 func funcSort(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 	// NaN should sort to the bottom, so take descending sort with NaN first and
@@ -355,19 +345,12 @@ func funcScalar(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 }
 
 func aggrOverTime(vals []Value, enh *EvalNodeHelper, aggrFn func([]Point) float64) Vector {
-	mat := vals[0].(Matrix)
+	el := vals[0].(Matrix)[0]
 
-	for _, el := range mat {
+	return append(enh.out, Sample{
-		if len(el.Points) == 0 {
-			continue
-		}
-
-		enh.out = append(enh.out, Sample{
 		Point: Point{V: aggrFn(el.Points)},
 	})
 }
-	return enh.out
-}
 
 // === avg_over_time(Matrix ValueTypeMatrix) Vector ===
 func funcAvgOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
@@ -429,23 +412,16 @@ func funcSumOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector
 // === quantile_over_time(Matrix ValueTypeMatrix) Vector ===
 func funcQuantileOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 	q := vals[0].(Vector)[0].V
-	mat := vals[1].(Matrix)
+	el := vals[1].(Matrix)[0]
-
-	for _, el := range mat {
-		if len(el.Points) == 0 {
-			continue
-		}
 
 	values := make(vectorByValueHeap, 0, len(el.Points))
 	for _, v := range el.Points {
 		values = append(values, Sample{Point: Point{V: v.V}})
 	}
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: quantile(q, values)},
 	})
 }
-	return enh.out
-}
 
 // === stddev_over_time(Matrix ValueTypeMatrix) Vector ===
 func funcStddevOverTime(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
@@ -588,45 +564,39 @@ func linearRegression(samples []Point, interceptTime int64) (slope, intercept fl
 
 // === deriv(node ValueTypeMatrix) Vector ===
 func funcDeriv(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
-	mat := vals[0].(Matrix)
+	samples := vals[0].(Matrix)[0]
 
-	for _, samples := range mat {
 	// No sense in trying to compute a derivative without at least two points.
 	// Drop this Vector element.
 	if len(samples.Points) < 2 {
-			continue
+		return enh.out
 	}
 
 	// We pass in an arbitrary timestamp that is near the values in use
 	// to avoid floating point accuracy issues, see
 	// https://github.com/prometheus/prometheus/issues/2674
 	slope, _ := linearRegression(samples.Points, samples.Points[0].T)
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: slope},
 	})
 }
-	return enh.out
-}
 
 // === predict_linear(node ValueTypeMatrix, k ValueTypeScalar) Vector ===
 func funcPredictLinear(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
-	mat := vals[0].(Matrix)
+	samples := vals[0].(Matrix)[0]
 	duration := vals[1].(Vector)[0].V
 
-	for _, samples := range mat {
 	// No sense in trying to predict anything without at least two points.
 	// Drop this Vector element.
 	if len(samples.Points) < 2 {
-			continue
+		return enh.out
 	}
 	slope, intercept := linearRegression(samples.Points, enh.ts)
 
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: slope*duration + intercept},
 	})
 }
-	return enh.out
-}
 
 // === histogram_quantile(k ValueTypeScalar, Vector ValueTypeVector) Vector ===
 func funcHistogramQuantile(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
@@ -678,9 +648,8 @@ func funcHistogramQuantile(vals []Value, args Expressions, enh *EvalNodeHelper)
 
 // === resets(Matrix ValueTypeMatrix) Vector ===
 func funcResets(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
-	in := vals[0].(Matrix)
+	samples := vals[0].(Matrix)[0]
 
-	for _, samples := range in {
 	resets := 0
 	prev := samples.Points[0].V
 	for _, sample := range samples.Points[1:] {
@@ -691,18 +660,15 @@ func funcResets(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 		prev = current
 	}
 
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: float64(resets)},
 	})
 }
-	return enh.out
-}
 
 // === changes(Matrix ValueTypeMatrix) Vector ===
 func funcChanges(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
-	in := vals[0].(Matrix)
+	samples := vals[0].(Matrix)[0]
 
-	for _, samples := range in {
 	changes := 0
 	prev := samples.Points[0].V
 	for _, sample := range samples.Points[1:] {
@@ -713,12 +679,10 @@ func funcChanges(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
 		prev = current
 	}
 
-		enh.out = append(enh.out, Sample{
+	return append(enh.out, Sample{
 		Point: Point{V: float64(changes)},
 	})
 }
-	return enh.out
-}
 
 // === label_replace(Vector ValueTypeVector, dst_label, replacement, src_labelname, regex ValueTypeString) Vector ===
 func funcLabelReplace(vals []Value, args Expressions, enh *EvalNodeHelper) Vector {
@@ -1297,7 +1261,7 @@ func createLabelsForAbsentFunction(expr Expr) labels.Labels {
 	}
 
 	for _, v := range empty {
-		m = labels.NewBuilder(m).Set(v, "").Labels()
+		m = labels.NewBuilder(m).Del(v).Labels()
 	}
 	return m
 }