[jira] [Updated] (ARROW-6481) [Python][C++] Bad performance of read_csv() with column_types

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

     [ https://issues.apache.org/jira/browse/ARROW-6481?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney updated ARROW-6481:
--------------------------------
    Summary: [Python][C++] Bad performance of read_csv() with column_types  (was: [Python] Bad performance of read_csv() with column_types)

> [Python][C++] Bad performance of read_csv() with column_types
> -------------------------------------------------------------
>
>                 Key: ARROW-6481
>                 URL: https://issues.apache.org/jira/browse/ARROW-6481
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++, Python
>    Affects Versions: 0.14.1
>         Environment: ubuntu xenial, python2.7
>            Reporter: Bogdan Klichuk
>            Priority: Major
>         Attachments: 20k_cols.csv
>
>
> Case: Dataset wit 20k columns. Amount of rows can be 0.
> {{pyarrow.csv.read_csv('20k_cols.csv')}} works rather fine if no convert_options provided.
> Took 150ms.
> Now I call {{read_csv()}} with column types mapping that marks 2000 out of these columns as string.
> {{pyarrow.csv.read_csv('20k_cols.csv', convert_options=pyarrow.csv.ConvertOptions(column_types=\{'K%d' % i: pyarrow.string() for i in range(2000)}))}}
> (K1..K19999 are column names in attached dataset).
> My task globally is to read everything as string, avoid any inferring.
> This takes several minutes, consumes around 4GB memory.
> This doesn't look sane at all.



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