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https://issues.apache.org/jira/browse/ARROW-6570?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wes McKinney resolved ARROW-6570.
---------------------------------
Resolution: Fixed
Issue resolved by pull request 5398
[https://github.com/apache/arrow/pull/5398]
> [Python] Use MemoryPool to allocate memory for NumPy arrays in to_pandas calls
> ------------------------------------------------------------------------------
>
> Key: ARROW-6570
> URL: https://issues.apache.org/jira/browse/ARROW-6570
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Wes McKinney
> Assignee: Wes McKinney
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.15.0
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> It occurred to me that we can likely improve the performance and scalability
> of {{Table.to_pandas}} or other {{to_pandas}} methods by using the active
> MemoryPool to allocate memory for the array rather than letting NumPy use the
> system allocator. We would need to use the {{PyCapsule}} approach to setting
> a {{shared_ptr<Buffer>}} as the base of the created NumPy arrays
> This has the additional benefit of tracking NumPy-related allocations in the
> MemoryPool so we will have a more precise accounting of allocated memory.
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