[GitHub] [spark] zhengruifeng edited a comment on issue #26948: [SPARK-30120][ML] LSH approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors is small

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zhengruifeng edited a comment on issue #26948: [SPARK-30120][ML] LSH approxNearestNeighbors should use BoundedPriorityQueue when numNearestNeighbors is small
URL: https://github.com/apache/spark/pull/26948#issuecomment-568335734
 
 
   @srowen @huaxingao 
   I rethinked this more, and I found another solution:
   1, current impl need a threshold, we no longer need `BoundedPriorityQueue`. Current impl compute count and quantile on two pass, we can just compute them together on only one pass. So the advantage that `BoundedPriorityQueue` do not need an extra pass does not exist, `approxQuantile` is enough, but we need to use `QuantileSummaries` directly;
   2, still for top-K when nn is small (maybe < 1000 at first?). Why not using `BoundedPriorityQueue` to directly collect the top-K **rows (now distCol for a threshold)**? 
   3, for the final `modelSubsetWithDistCol.sort(distCol).limit(numNearestNeighbors)`. when nn is small, using `BoundedPriorityQueue` to avoid global sort.

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