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https://issues.apache.org/jira/browse/ARROW-6874?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16951314#comment-16951314
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Joris Van den Bossche commented on ARROW-6874:
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This seems to be caused by ARROW-6570
(https://github.com/apache/arrow/commit/19545f878d17f99a07e51e818eddc8c77f38f56b).
The problem comes up for object-dtype arrays (so for list, struct, string
dtype)
> [Python] Memory leak in Table.to_pandas() when nested columns are present
> -------------------------------------------------------------------------
>
> Key: ARROW-6874
> URL: https://issues.apache.org/jira/browse/ARROW-6874
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.15.0
> Environment: Operating system: Windows 10
> pyarrow installed via conda
> both python environments were identical except pyarrow:
> python: 3.6.7
> numpy: 1.17.2
> pandas: 0.25.1
> Reporter: Sergey Mozharov
> Priority: Major
> Fix For: 1.0.0, 0.15.1
>
>
> I upgraded from pyarrow 0.14.1 to 0.15.0 and during some testing my python
> interpreter ran out of memory.
> I narrowed the issue down to the pyarrow.Table.to_pandas() call, which
> appears to have a memory leak in the latest version. See details below to
> reproduce this issue.
>
> {code:java}
> import numpy as np
> import pandas as pd
> import pyarrow as pa
> # create a table with one nested array column
> nested_array = pa.array([np.random.rand(1000) for i in range(500)])
> nested_array.type # ListType(list<item: double>)
> table = pa.Table.from_arrays(arrays=[nested_array], names=['my_arrays'])
> # convert it to a pandas DataFrame in a loop to monitor memory consumption
> num_iterations = 10000
> # pyarrow v0.14.1: Memory allocation does not grow during loop execution
> # pyarrow v0.15.0: ~550 Mb is added to RAM, never garbage collected
> for i in range(num_iterations):
> df = pa.Table.to_pandas(table)
> # When the table column is not nested, no memory leak is observed
> array = pa.array(np.random.rand(500 * 1000))
> table = pa.Table.from_arrays(arrays=[array], names=['numbers'])
> # no memory leak:
> for i in range(num_iterations):
> df = pa.Table.to_pandas(table){code}
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