Github user tgravescs commented on the issue:
https://github.com/apache/spark/pull/15297
Ok so how does that affect the overall job and # of outputs? I don't know
the internals of Spark SQL so sorry if I'm missing something obvious.
Basically now you will have multiple tasks whereas it used to use 1. So lets
say I have spark.sql.shuffle.partitions=200 to start with, the skewed join add
tasks to process some skewed partition so lets say it runs 210 tasks, then lets
say I save that to HDFS, do I get 210 output files or does it join those 10
back into 1 again?
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