[jira] [Updated] (ARROW-9974) [Python][C++] pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset

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

     [ https://issues.apache.org/jira/browse/ARROW-9974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney updated ARROW-9974:
--------------------------------
    Priority: Critical  (was: Major)

> [Python][C++] pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset
> ---------------------------------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-9974
>                 URL: https://issues.apache.org/jira/browse/ARROW-9974
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++, Python
>            Reporter: Ashish Gupta
>            Priority: Critical
>              Labels: dataset
>             Fix For: 2.0.0
>
>         Attachments: legacy_false.txt, legacy_true.txt
>
>
> [https://stackoverflow.com/questions/63792849/pyarrow-version-1-0-bug-throws-out-of-memory-exception-while-reading-large-numbe]
> I have a dataframe split and stored in more than 5000 files. I use ParquetDataset(fnames).read() to load all files. I updated the pyarrow to latest version 1.0.1 from 0.13.0 and it has started throwing "OSError: Out of memory: malloc of size 131072 failed". The same code on the same machine still works with older version. My machine has 256Gb memory way more than enough to load the data which requires < 10Gb. You can use below code to generate the issue on your side.
> {code}
>     # create a big dataframe
>     import pandas as pd
>     import numpy as np
>     df = pd.DataFrame({'A': np.arange(50000000)})
>     df['F1'] = np.random.randn(50000000) * 100
>     df['F2'] = np.random.randn(50000000) * 100
>     df['F3'] = np.random.randn(50000000) * 100
>     df['F4'] = np.random.randn(50000000) * 100
>     df['F5'] = np.random.randn(50000000) * 100
>     df['F6'] = np.random.randn(50000000) * 100
>     df['F7'] = np.random.randn(50000000) * 100
>     df['F8'] = np.random.randn(50000000) * 100
>     df['F9'] = 'ABCDEFGH'
>     df['F10'] = 'ABCDEFGH'
>     df['F11'] = 'ABCDEFGH'
>     df['F12'] = 'ABCDEFGH01234'
>     df['F13'] = 'ABCDEFGH01234'
>     df['F14'] = 'ABCDEFGH01234'
>     df['F15'] = 'ABCDEFGH01234567'
>     df['F16'] = 'ABCDEFGH01234567'
>     df['F17'] = 'ABCDEFGH01234567'
>     # split and save data to 5000 files
>     for i in range(5000):
>         df.iloc[i*10000:(i+1)*10000].to_parquet(f'{i}.parquet', index=False)
>     # use a fresh session to read data
>     # below code works to read
>     import pandas as pd
>     df = []
>     for i in range(5000):
>         df.append(pd.read_parquet(f'{i}.parquet'))
>     df = pd.concat(df)
>     # below code crashes with memory error in pyarrow 1.0/1.0.1 (works fine with version 0.13.0)
>     # tried use_legacy_dataset=False, same issue
>     import pyarrow.parquet as pq
>     fnames = []
>     for i in range(5000):
>         fnames.append(f'{i}.parquet')
>     len(fnames)
>     df = pq.ParquetDataset(fnames).read(use_threads=False)
>  
>  {code}



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