[jira] [Created] (ARROW-9974) pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset (works fine with version 0.13)

["Ashish Gupta (Jira)" <ji...@apache.org>]

Ashish Gupta created ARROW-9974:
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             Summary: pyarrow version 1.0.1 throws Out Of Memory exception while reading large number of files using ParquetDataset (works fine with version 0.13)
                 Key: ARROW-9974
                 URL: https://issues.apache.org/jira/browse/ARROW-9974
             Project: Apache Arrow
          Issue Type: Bug
            Reporter: Ashish Gupta


[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.

 
    # 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)
 

 



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