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https://issues.apache.org/jira/browse/ARROW-7150?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Bogdan Klichuk updated ARROW-7150:
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Environment: Mac OS X. Pyarrow==0.14.1 (was: Mac OS X. Pyarrow==0.15.1)
> [Python] Explain parquet file size growth
> -----------------------------------------
>
> Key: ARROW-7150
> URL: https://issues.apache.org/jira/browse/ARROW-7150
> Project: Apache Arrow
> Issue Type: Task
> Components: Python
> Affects Versions: 0.14.1
> Environment: Mac OS X. Pyarrow==0.14.1
> Reporter: Bogdan Klichuk
> Priority: Major
>
> Having columnar storage format in mind, with gzip compression enabled, I
> can't make sense of how parquet file size is growing in my specific example.
> So far without sharing a dataset (would need to create a mock one to share).
> {code:java}
> > df = pandas.read_csv('...')
> > len(df)
> 820
> > # 1. write 820 rows to a parquet file
> > df.to_parquet('820.parquet', compression='gzip)
> > # size of 820.parquet is 6.1M
> > # 2. write 8200 rows to a parquet file
> > df_big = pandas.concat([df] * 10).reset_index(drop=True)
> > len(df_big)
> 8200
> > df_big.to_parquet('8200.parquet', compression='gzip')
> > # size of 800.parquet is 320M.
> {code}
>
>
> Compression works better on bigger files. How come 10x1 increase with
> repeated data resulted in 50x growth of file? Insane imo.
>
> Working on a periodic job that concats smaller files into bigger ones and
> doubting now whether I need this.
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