[jira] [Resolved] (ARROW-6570) [Python] Use MemoryPool to allocate memory for NumPy arrays in to_pandas calls

["Wes McKinney (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-6570?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

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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