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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2020/01/06 06:32:00 UTC
[jira] [Resolved] (SPARK-30316) data size boom after shuffle
writing dataframe save as parquet
[ https://issues.apache.org/jira/browse/SPARK-30316?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-30316.
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Resolution: Invalid
Please show reproducible steps and output files (at least {{ls -al}}). Otherwise, no one knows what issue you faced.
> 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
> Priority: Major
>
> 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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