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https://issues.apache.org/jira/browse/ARROW-7305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17000893#comment-17000893
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Bogdan Klichuk commented on ARROW-7305:
---------------------------------------

I have tried this in ubuntu docker and results for 0.14.1 vs 0.15.1 are pretty 
interesting.

 

0.14.1:
{code:java}
 Line #    Mem usage    Increment   Line Contents
================================================
     4     50.5 MiB     50.5 MiB   @profile
     5                             def do():
     6     99.9 MiB     49.4 MiB       df = pd.read_csv('50mb.csv')
     7    112.1 MiB     12.1 MiB       df.to_parquet('test.parquet'){code}
0.15.1:
{code:java}
Line #    Mem usage    Increment   Line Contents
================================================
     4     50.5 MiB     50.5 MiB   @profile
     5                             def do():
     6    100.0 MiB     49.4 MiB       df = pd.read_csv('50mb.csv')
     7    401.4 MiB    301.4 MiB       df.to_parquet('test.parquet') {code}
which besides the fact that 0.14.1 does indeed behave better on non-mac, also 
shows that 0.15.1 requires much more memory to write.

 

> [Python] High memory usage writing pyarrow.Table with large strings 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
>              Labels: parquet
>         Attachments: 50mb.csv.gz
>
>
> 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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