Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15800
I think that we would have the following hash distance signature:
````scala
def hashDistance(x: Vector, y: Vector): Double
````
Then in `approxNearestNeighbors` we would explode the `Array[Vector]`
column, then apply hash distance and sort on that distance. That way we first
select any point where ANY of the L g_l(x) vectors match, and only after that
do we consider hashes that are close in the distance measure.
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