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https://issues.apache.org/jira/browse/ARROW-10739?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17610662#comment-17610662
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Clark Zinzow commented on ARROW-10739:
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[~jorisvandenbossche] I did a quick implementation of (2), where the Arrow IPC
format is used under-the-hood for pickle serialization, and confirmed that the
buffer truncation works as expected. Although this is a far simpler solution
than (1), the overhead of the `RecordBatch` wrapper adds ~230 extra bytes to
the pickled payload (per `Array` chunk) compared to current Arrow master, which
can be pretty bad for the many-chunk and/or many-column case (order of
magnitude larger serialized payloads). We could sidestep this issue by having
`Table`, `RecordBatch`, and `ChunkedArray` port their `__reduce__` to the Arrow
IPC serialization as well, which should avoid this many-column and many-chunk
blow-up, but there will still be the baseline ~230 byte bloat for
`ChunkedArray` and `Array` that we might find untenable.
I can try to get a PR up for (2) either today or tomorrow while I start working
on (1) in the background. (1) is going to have a much larger Arrow code impact
+ we'll continue having two serialization paths to maintain, but it shouldn't
result in any serialized payload bloat.
> [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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