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@HyukjinKwon
Thank you so much ! Really helpful ð
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Github user jinxing64 commented on the issue:
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Gentle ping to @JoshRosen @cloud-fan @mridulm
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Jenkins, retest this please
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I try to give user a way to control the memory strictly and no blocks are
underestimated(setting spark.shuffle.accurateBlockThreshold=0 and
spark.shuffle.accurateBlockThresholdByTimesAverage=1).
Github user jinxing64 commented on the issue:
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To resolve the comments in https://github.com/apache/spark/pull/16989 :
>minimum size before we consider something a large block : if average is
10kb, and some blocks are > 20kb, spilling them
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