zhengruifeng 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_r358620968
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/LSH.scala ########## @@ -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: A slight performance gain may come from that ` BoundedPriorityQueue` do not need a `count` job to compute the var `approxQuantile`. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org