[jira] [Updated] (ARROW-18400) [Python] Quadratic memory usage of Table.to_pandas with nested data

["Alenka Frim (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-18400?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Alenka Frim updated ARROW-18400:
--------------------------------
    Attachment: test_memory.py

> [Python] Quadratic memory usage of Table.to_pandas with nested data
> -------------------------------------------------------------------
>
>                 Key: ARROW-18400
>                 URL: https://issues.apache.org/jira/browse/ARROW-18400
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 10.0.1
>         Environment: Python 3.10.8 on Fedora Linux 36. AMD Ryzen 9 5900 X with 64 GB RAM
>            Reporter: Adam Reeve
>            Assignee: Alenka Frim
>            Priority: Critical
>             Fix For: 11.0.0
>
>         Attachments: test_memory.py
>
>
> Reading nested Parquet data and then converting it to a Pandas DataFrame shows quadratic memory usage and will eventually run out of memory for reasonably small files. I had initially thought this was a regression since 7.0.0, but it looks like 7.0.0 has similar quadratic memory usage that kicks in at higher row counts.
> Example code to generate nested Parquet data:
> {code:python}
> import numpy as np
> import random
> import string
> import pandas as pd
> _characters = string.ascii_uppercase + string.digits + string.punctuation
> def make_random_string(N=10):
>     return ''.join(random.choice(_characters) for _ in range(N))
> nrows = 1_024_000
> filename = 'nested.parquet'
> arr_len = 10
> nested_col = []
> for i in range(nrows):
>     nested_col.append(np.array(
>             [{
>                 'a': None if i % 1000 == 0 else np.random.choice(10000, size=3).astype(np.int64),
>                 'b': None if i % 100 == 0 else random.choice(range(100)),
>                 'c': None if i % 10 == 0 else make_random_string(5)
>             } for i in range(arr_len)]
>         ))
> df = pd.DataFrame({'c1': nested_col})
> df.to_parquet(filename)
> {code}
> And then read into a DataFrame with:
> {code:python}
> import pyarrow.parquet as pq
> table = pq.read_table(filename)
> df = table.to_pandas()
> {code}
> Only reading to an Arrow table isn't a problem, it's the to_pandas method that exhibits the large memory usage. I haven't tested generating nested Arrow data in memory without writing Parquet from Pandas but I assume the problem probably isn't Parquet specific.
> Memory usage I see when reading different sized files on a machine with 64 GB RAM:
> ||Num rows||Memory used with 10.0.1 (MB)||Memory used with 7.0.0 (MB)||
> |32,000|362|361|
> |64,000|531|531|
> |128,000|1,152|1,101|
> |256,000|2,888|1,402|
> |512,000|10,301|3,508|
> |1,024,000|38,697|5,313|
> |2,048,000|OOM|20,061|
> |4,096,000| |OOM|
> With Arrow 10.0.1, memory usage approximately quadruples when row count doubles above 256k rows. With Arrow 7.0.0 memory usage is more linear but then quadruples from 1024k to 2048k rows.
> PyArrow 8.0.0 shows similar memory usage to 10.0.1 so it looks like something changed between 7.0.0 and 8.0.0.



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