BUG: random crash / hang when calculating rolling sum

[e271p314]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import numpy as np
import pandas as pd

def func():
    cols = ['A', 'B', 'C']
    df = pd.DataFrame({c: np.random.randn(500000) for c in cols})
    sec_lst = [60 * m for m in range(1, 10)]
    sec_lst += [60 * m for m in range(10, 30, 5)]
    sec_lst += [60 * m for m in range(30, 60, 10)]
    sec_lst += [60 * m for m in range(60, 120, 15)]
    sec_lst += [60 * m for m in range(120, 180, 20)]
    sec_lst += [60 * m for m in range(180, 600, 30)]
    sec_lst += [3600 * h for h in range(10, 24)]
    df_sum_dict = {}
    for sec in sec_lst:
        for c in cols:
            try:
                df_sum_dict[f'{c}{sec}'] = df[c].rolling(sec).sum()
            except Exception as e:
                print(f"Error processing column {c} with window {sec}: {e}")
                continue

func()

Issue Description

running with this loop results in the following output (Terminated is the output when I kill it because the script hangs, should run few seconds at most)

$ for i in $(seq 1 100); do python test.py $i; if [ $? -ne 0 ]; then echo $i; fi; done
Segmentation fault (core dumped)
6
Terminated
24
Terminated
41
Segmentation fault (core dumped)
42
Segmentation fault (core dumped)
43
Terminated
44
Segmentation fault (core dumped)
45
Terminated
64
Terminated
65
Segmentation fault (core dumped)
74
Segmentation fault (core dumped)
95

Expected Behavior

it should not crash or hang

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.12.3.final.0
python-bits : 64
OS : Linux
OS-release : 6.8.0-35-generic
Version : #35-Ubuntu SMP PREEMPT_DYNAMIC Mon May 20 15:51:52 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 70.0.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.25.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.4.0
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.0
gcsfs : 2024.6.0
matplotlib : 3.9.0
numba : 0.59.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 16.1.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : 2.0.30
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[chaoyihu]

Hi, thanks for raising the issue! I was unable to reproduce the random crash/hang you experienced in any of the following envs:

Installed versions 1 (main)

commit                : 5e972376fd5e4ab033f9922b546495d8efc9fda5
python                : 3.10.14.final.0
python-bits           : 64
OS                    : Linux
OS-release            : 6.5.0-41-generic
Version               : #41~22.04.2-Ubuntu SMP PREEMPT_DYNAMIC Mon Jun  3 11:32:55 UTC 2
machine               : x86_64
processor             : x86_64
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8
pandas                : 3.0.0.dev0+1026.g5e972376fd

numpy                 : 1.26.4

pytz                  : 2024.1

dateutil              : 2.9.0.post0

setuptools            : 69.0.3

pip                   : 24.1

Cython                : 3.0.8

pytest                : 8.0.2

hypothesis            : 6.103.2

sphinx                : 7.3.7

blosc                 : None

feather               : None

xlsxwriter            : 3.1.9

lxml.etree            : 5.2.2

html5lib              : 1.1

pymysql               : 1.4.6

psycopg2              : 2.9.9

jinja2                : 3.1.2

IPython               : 8.12.3

pandas_datareader     : None

adbc-driver-postgresql: None

adbc-driver-sqlite    : None

bs4                   : 4.12.3

bottleneck            : 1.4.0

fastparquet           : 2024.5.0

fsspec                : 2024.6.0

gcsfs                 : 2024.6.0

matplotlib            : 3.9.0

numba                 : 0.59.1

numexpr               : 2.10.0

odfpy                 : None

openpyxl              : 3.1.4

pyarrow               : 15.0.2

pyreadstat            : 1.2.7

python-calamine       : None

pyxlsb                : 1.0.10

s3fs                  : 2024.6.0

scipy                 : 1.12.0

sqlalchemy            : 2.0.31

tables                : 3.9.2

tabulate              : 0.9.0

xarray                : 2024.6.0

xlrd                  : 2.0.1

zstandard             : 0.22.0

tzdata                : 2024.1

qtpy                  : 2.4.1

pyqt5                 : None

Installed versions 2 (close to the one provided in issue description)

commit                : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140
python                : 3.12.3.final.0
python-bits           : 64
OS                    : Linux
OS-release            : 6.5.0-41-generic
Version               : #41~22.04.2-Ubuntu SMP PREEMPT_DYNAMIC Mon Jun  3 11:32:55 UTC 2
machine               : x86_64
processor             : x86_64
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8
pandas                : 2.2.2

numpy                 : 1.26.4

pytz                  : 2024.1

dateutil              : 2.9.0

setuptools            : 70.1.1

pip                   : 24.0

Cython                : 3.0.10

pytest                : 8.2.2

hypothesis            : 6.104.1

sphinx                : 7.3.7

blosc                 : None

feather               : None

xlsxwriter            : 3.1.9

lxml.etree            : 5.2.2

html5lib              : 1.1

pymysql               : 1.4.6

psycopg2              : 2.9.9

jinja2                : 3.1.4

IPython               : 8.25.0

pandas_datareader     : None

adbc-driver-postgresql: None

adbc-driver-sqlite    : None

bs4                   : 4.12.3

bottleneck            : 1.4.0

dataframe-api-compat  : None

fastparquet           : 2024.5.0

fsspec                : 2024.6.0

gcsfs                 : 2024.6.0

matplotlib            : 3.8.4

numba                 : 0.60.0

numexpr               : 2.10.0

odfpy                 : None

openpyxl              : 3.1.4

pandas_gbq            : None

pyarrow               : 16.1.0

pyreadstat            : 1.2.7

python-calamine       : None

pyxlsb                : 1.0.10

s3fs                  : 2024.6.0

scipy                 : 1.14.0

sqlalchemy            : 2.0.31

tables                : 3.9.2

tabulate              : 0.9.0

xarray                : 2024.6.0

xlrd                  : 2.0.1

zstandard             : 0.22.0

tzdata                : 2024.1

qtpy                  : None

pyqt5                 : None

Since the reproducible script here is self-contained and works on my machine, I suspect this is not a problem with pandas or any of its dependencies. My intuition would be that the Segmentation fault (core dumped) errors here suggest low memory or even hardware issues - here is a potentially relevant post.

I would suggest double-checking memory usage / running the code on a different machine to see if the error persists.

[e271p314]

Wow! you are right!
sudo swapoff -a
solved the problem for me, I would never never guess, thank you very much!