[jira] [Updated] (ARROW-11469) Performance degradation wide dataframes

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-11469?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joris Van den Bossche updated ARROW-11469:
------------------------------------------
    Description: 
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.

  was:
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)
pd.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.


> Performance degradation 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
>
> 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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