Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/8125#issuecomment-131669839
@liancheng we have this cases:
The production system produce small text/csv files every five minute, and
we use spark sql to do some ETL work(such as agg) on this small files to
produce the agg parquet/orc files to for analysis. In this case this small
files only go through once, so we do not want to do compacting periodically
here and hope that spark sql can merge the small files when do the ETL work. We
have test this case with this patch, and performance improved about 20%.
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