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https://issues.apache.org/jira/browse/ARROW-10739?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17571590#comment-17571590
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Philipp Moritz commented on ARROW-10739:
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In Ray we are also planning to work around this
[https://github.com/ray-project/ray/pull/22891] – it would be wonderful to see
this fixed in Arrow :)
> [Python] Pickling a sliced array serializes all the buffers
> -----------------------------------------------------------
>
> Key: ARROW-10739
> URL: https://issues.apache.org/jira/browse/ARROW-10739
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Reporter: Maarten Breddels
> Assignee: Alessandro Molina
> Priority: Critical
> Fix For: 10.0.0
>
>
> If a large array is sliced, and pickled, it seems the full buffer is
> serialized, this leads to excessive memory usage and data transfer when using
> multiprocessing or dask.
> {code:java}
> >>> import pyarrow as pa
> >>> ar = pa.array(['foo'] * 100_000)
> >>> ar.nbytes
> 700004
> >>> import pickle
> >>> len(pickle.dumps(ar.slice(10, 1)))
> 700165
> NumPy for instance
> >>> import numpy as np
> >>> ar_np = np.array(ar)
> >>> ar_np
> array(['foo', 'foo', 'foo', ..., 'foo', 'foo', 'foo'], dtype=object)
> >>> import pickle
> >>> len(pickle.dumps(ar_np[10:11]))
> 165{code}
> I think this makes sense if you know arrow, but kind of unexpected as a user.
> Is there a workaround for this? For instance copy an arrow array to get rid
> of the offset, and trim the buffers?
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