Github user YuhuWang2002 commented on the issue:
https://github.com/apache/spark/pull/15297
@tgravescs ï¼
Thank you for your response, when a single reduce task handling huge data,
it's slowly and unstable. so we split one reduce task to multi- reduce task.
A single reduce task doing like A join B. we split to multi-task. task 1
doing A1 join B, task 2 dong A2 join B and so on. A1 is a part of A which
read from a range of maps output. For spark sql, it is the A1 as a separate
partitions when processing. so it can use mutil-executor to run the task. for
dispersion the process pressure.
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