[GitHub] [spark] srowen commented on a change in pull request #26948: [SPARK-30120][ML] LSH approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors is small

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srowen commented on a change in pull request #26948: [SPARK-30120][ML] LSH approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors is small
URL: https://github.com/apache/spark/pull/26948#discussion_r360901804
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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 @@ -138,21 +137,37 @@ private[ml] abstract class LSHModel[T <: LSHModel[T]]
       // Limit the use of hashDist since it's controversial
       val hashDistUDF = udf((x: Seq[Vector]) => hashDistance(x, keyHash), DataTypes.DoubleType)
       val hashDistCol = hashDistUDF(col($(outputCol)))
-
-      // Compute threshold to get around k elements.
-      // To guarantee to have enough neighbors in one pass, we need (p - err) * N >= M
-      // so we pick quantile p = M / N + err
-      // M: the number of nearest neighbors; N: the number of elements in dataset
-      val relativeError = 0.05
-      val approxQuantile = numNearestNeighbors.toDouble / count + relativeError
       val modelDatasetWithDist = modelDataset.withColumn(distCol, hashDistCol)
-      if (approxQuantile >= 1) {
-        modelDatasetWithDist
+
+      if (numNearestNeighbors < 1000) {
 
 Review comment:
   I think I have the same question as in the other PR: if this is faster than quantiles for small neighbors, then I'd expect it's faster for everything. I don't know if it is though? my guess is that it wouldn't be. You save the count() but the count() isn't particularly expensive. The question might be how much that saves and at what scale.

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