Cesc created SPARK-30316:
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Summary: data size boom after shuffle writing dataframe save as
parquet
Key: SPARK-30316
URL: https://issues.apache.org/jira/browse/SPARK-30316
Project: Spark
Issue Type: Improvement
Components: Shuffle, SQL
Affects Versions: 2.4.4
Reporter: Cesc
When I read a same parquet file and then save it in two ways, with shuffle and
without shuffle, I found the size of output parquet files are quite different.
For example, an origin parquet file with 800 MB size, if save without shuffle,
the size is still 800MB, whereas if I use method repartition and then save it
as in parquet format, the data size increase to 2.5GB. Row numbers, column
numbers and content of two output files are all the same.
I wonder:
firstly, why data size will increase after repartition/shuffle?
secondly, if I need shuffle the input dataframe, how to save it as parquet file
efficiently to avoid data size boom?
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