Github user viirya commented on the issue:
https://github.com/apache/spark/pull/16633
@scwf
> it use a special partitioner to do this, the partitioner like the
row_numer in sql it give each row a uniform partitionid, so in the reduce task,
each task handle num of rows very closely.
I see @wzhfy wants to use a partitioner to uniformly distribute the rows in
each local limit. However, because each local limit can produce different
number of rows, you can't get a real uniform distribution. So in the global
limit operation, you can't know how many partitions you need to use in order to
satisfy the final limit number.
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