Repository: prometheus/prometheus
File: promql/functions.go (Go)

Code before:
		it := sample.H.AllBucketIterator()
		for it.Next() {
			bucket := it.At()
			if bucket.Count == 0 {
				continue
			}
			var val float64
			if bucket.Lower <= 0 && 0 <= bucket.Upper {
				val = 0
			} else {
				val = math.Sqrt(bucket.Upper * bucket.Lower)
				if bucket.Upper < 0 {
					val = -val
				}
			}
			delta := val - mean
			variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
		}
		variance += cVariance
		variance /= sample.H.Count
		if !enh.enableDelayedNameRemoval {
			sample.Metric = sample.Metric.DropMetricName()
		}
		enh.Out = append(enh.Out, Sample{
			Metric:   sample.Metric,
			F:        varianceToResult(variance),
			DropName: true,
		})
	}
	return enh.Out, nil
}

// === histogram_stddev(Vector parser.ValueTypeVector) (Vector, Annotations)  ===
func funcHistogramStdDev(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	inVec := vals[0].(Vector)
	return histogramVariance(inVec, enh, math.Sqrt)
}

// === histogram_stdvar(Vector parser.ValueTypeVector) (Vector, Annotations) ===
func funcHistogramStdVar(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	inVec := vals[0].(Vector)
	return histogramVariance(inVec, enh, func(f float64) float64 {
		return f
	})
}

// === histogram_fraction(lower, upper parser.ValueTypeScalar, Vector parser.ValueTypeVector) (Vector, Annotations) ===
func funcHistogramFraction(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	lower := vals[0].(Vector)[0].F
	upper := vals[1].(Vector)[0].F
	inVec := vals[2].(Vector)

Review comment (suggestion):
Continuation of above suggestion:
```suggestion
			F:        variance,
```

Code after:
		}
		mean := sample.H.Sum / sample.H.Count
		var variance, cVariance float64
		it := sample.H.AllBucketIterator()
		for it.Next() {
			bucket := it.At()
			if bucket.Count == 0 {
				continue
			}
			var val float64
			if bucket.Lower <= 0 && 0 <= bucket.Upper {
				val = 0
			} else {
				val = math.Sqrt(bucket.Upper * bucket.Lower)
				if bucket.Upper < 0 {
					val = -val
				}
			}
			delta := val - mean
			variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
		}
		variance += cVariance
		variance /= sample.H.Count
		if !enh.enableDelayedNameRemoval {
			sample.Metric = sample.Metric.DropMetricName()
		}
		if varianceToResult != nil {
			variance = varianceToResult(variance)
		}
		enh.Out = append(enh.Out, Sample{
			Metric:   sample.Metric,
			F:        variance,
			DropName: true,
		})
	}
	return enh.Out, nil
}

// === histogram_stddev(Vector parser.ValueTypeVector) (Vector, Annotations)  ===
func funcHistogramStdDev(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	return histogramVariance(vals, enh, math.Sqrt)
}

// === histogram_stdvar(Vector parser.ValueTypeVector) (Vector, Annotations) ===
func funcHistogramStdVar(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	return histogramVariance(vals, enh, nil)
}

// === histogram_fraction(lower, upper parser.ValueTypeScalar, Vector parser.ValueTypeVector) (Vector, Annotations) ===
func funcHistogramFraction(vals []parser.Value, _ parser.Expressions, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
	lower := vals[0].(Vector)[0].F