[
https://issues.apache.org/jira/browse/ARROW-9974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ben Kietzman updated ARROW-9974:
--------------------------------
Description:
[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}
was:
[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)
> [Python][C++] 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
> Components: C++, Python
> Reporter: Ashish Gupta
> Priority: Major
> Labels: dataset
>
> [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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