[jira] [Updated] (ARROW-7305) [Python] High memory usage writing pyarrow.Table to parquet

["Wes McKinney (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-7305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney updated ARROW-7305:
--------------------------------
    Summary: [Python] High memory usage writing pyarrow.Table to parquet  (was: High memory usage writing pyarrow.Table to parquet)

> [Python] High memory usage writing pyarrow.Table to parquet
> -----------------------------------------------------------
>
>                 Key: ARROW-7305
>                 URL: https://issues.apache.org/jira/browse/ARROW-7305
>             Project: Apache Arrow
>          Issue Type: Task
>          Components: Python
>    Affects Versions: 0.15.1
>         Environment: Mac OSX
>            Reporter: Bogdan Klichuk
>            Priority: Major
>
> My case of datasets stored is specific. I have large strings (1-100MB each).
> Let's take for example a single row.
> 43mb.csv is a 1-row CSV with 10 columns. One column a 43mb string.
> When I read this csv with pandas and then dump to parquet, my script consumes 10x of the 43mb.
> With increasing amount of such rows memory footprint overhead diminishes, but I want to focus on this specific case.
> Here's the footprint after running using memory profiler:
> {code:java}
> Line #    Mem usage    Increment   Line Contents
> ================================================
>      4     48.9 MiB     48.9 MiB   @profile
>      5                             def test():
>      6    143.7 MiB     94.7 MiB       data = pd.read_csv('43mb.csv')
>      7    498.6 MiB    354.9 MiB       data.to_parquet('out.parquet')
>  {code}
> Is this typical for parquet in case of big strings?



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