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https://issues.apache.org/jira/browse/ARROW-11469?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17344639#comment-17344639
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Neal Richardson commented on ARROW-11469:
-----------------------------------------
Forgive me if I'm missing what the actual problem is, but would a solution be
to solve this case (no projection, or perhaps only column selection) in the
higher layers (Python, R, etc.)? ScannerBuilder->Project can either take a
vector of Expressions or a vector of string column names. Python/R could choose
to Project with strings if it is only selecting columns and not
modifying/deriving anything (and not Project at all if selecting everything).
> [Python] Performance degradation parquet reading of wide dataframes
> -------------------------------------------------------------------
>
> Key: ARROW-11469
> URL: https://issues.apache.org/jira/browse/ARROW-11469
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 1.0.0, 1.0.1, 2.0.0, 3.0.0
> Reporter: Axel G
> Priority: Minor
> Attachments: image-2021-05-03-14-31-41-260.png,
> image-2021-05-03-14-39-59-485.png, image-2021-05-03-14-40-09-520.png,
> profile_wide300.svg
>
>
> I noticed a relatively big performance degradation in version 1.0.0+ when
> trying to load wide dataframes.
> For example you should be able to reproduce by doing:
> {code:java}
> import numpy as np
> import pandas as pd
> import pyarrow as pa
> import pyarrow.parquet as pq
> df = pd.DataFrame(np.random.rand(100, 10000))
> table = pa.Table.from_pandas(df)
> pq.write_table(table, "temp.parquet")
> %timeit pd.read_parquet("temp.parquet"){code}
> In version 0.17.0, this takes about 300-400 ms and for anything above and
> including 1.0.0, this suddenly takes around 2 seconds.
>
> Thanks for looking into this.
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