Github user srowen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12016#discussion_r57732511
  
    --- Diff: core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala 
---
    @@ -40,30 +41,39 @@ private[spark] class SumEvaluator(totalOutputs: Int, 
confidence: Double)
       override def currentResult(): BoundedDouble = {
         if (outputsMerged == totalOutputs) {
           new BoundedDouble(counter.sum, 1.0, counter.sum, counter.sum)
    -    } else if (outputsMerged == 0) {
    +    } else if (outputsMerged == 0 || counter.count == 0) {
           new BoundedDouble(0, 0.0, Double.NegativeInfinity, 
Double.PositiveInfinity)
         } else {
           val p = outputsMerged.toDouble / totalOutputs
           val meanEstimate = counter.mean
    -      val meanVar = counter.sampleVariance / counter.count
           val countEstimate = (counter.count + 1 - p) / p
    -      val countVar = (counter.count + 1) * (1 - p) / (p * p)
           val sumEstimate = meanEstimate * countEstimate
    -      val sumVar = (meanEstimate * meanEstimate * countVar) +
    -                   (countEstimate * countEstimate * meanVar) +
    -                   (meanVar * countVar)
    -      val sumStdev = math.sqrt(sumVar)
    -      val confFactor = {
    -        if (counter.count > 100) {
    +
    +      val meanVar = counter.sampleVariance / counter.count
    +
    +      // branch at this point because counter.count == 1 implies 
counter.sampleVariance == Nan
    +      // and we don't want to ever return a bound of NaN
    +      if (meanVar == Double.NaN || counter.count == 1) {
    +        new BoundedDouble(sumEstimate, confidence, 
Double.NegativeInfinity, Double.PositiveInfinity)
    +      } else {
    +        val countVar = (counter.count + 1) * (1 - p) / (p * p)
    +        val sumVar = (meanEstimate * meanEstimate * countVar) +
    +          (countEstimate * countEstimate * meanVar) +
    +          (meanVar * countVar)
    +        val sumStdev = math.sqrt(sumVar)
    +        val confFactor = if (counter.count > 100) {
               new NormalDistribution().inverseCumulativeProbability(1 - (1 - 
confidence) / 2)
    -        } else {
    +        } else if (counter.count > 1) {
               val degreesOfFreedom = (counter.count - 1).toInt
               new 
TDistribution(degreesOfFreedom).inverseCumulativeProbability(1 - (1 - 
confidence) / 2)
    +        } else {
    +          throw new Exception("Counter.count <= 1; this should be 
impossible at this point")
    --- End diff --
    
    You've already handled the count=0 and count=1 cases earlier. Checking 
count > 1 doesn't do anything since it can't happen so having a branch for it 
is odd. Tests are how we catch regressions.


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