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

["Bogdan Klichuk (Jira)" <ji...@apache.org>]

Bogdan Klichuk created ARROW-7305:
-------------------------------------

             Summary: 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


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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