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https://issues.apache.org/jira/browse/ARROW-6481?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated ARROW-6481:
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Labels: pull-request-available (was: )
> [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
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> 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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