[jira] [Commented] (ARROW-10739) [Python] Pickling a sliced array serializes all the buffers

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-10739?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17241452#comment-17241452 ] 

Joris Van den Bossche commented on ARROW-10739:
-----------------------------------------------

Note that {{pyarrow.serialize}} is deprecated, so best not use that as a workaround

> [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
>            Priority: Major
>
> 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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