[GitHub] [spark] srowen commented on a change in pull request #26858: [SPARK-30120][ML] Use BoundedPriorityQueue for small dataset in LSH approxNearestNeighbors

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srowen commented on a change in pull request #26858: [SPARK-30120][ML] Use BoundedPriorityQueue for small dataset in LSH approxNearestNeighbors
URL: https://github.com/apache/spark/pull/26858#discussion_r357173270
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala
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 @@ -138,21 +139,31 @@ 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
+      // for a small dataset, use BoundedPriorityQueue
+      if (count < 1000) {
+        val queue = new BoundedPriorityQueue[Double](count.toInt)(Ordering[Double])
 
 Review comment:
   Shouldn't the queue just need numNearestNeighbors elements?

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