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Posted to jira@arrow.apache.org by "Joris Van den Bossche (Jira)" <ji...@apache.org> on 2021/05/03 12:55:00 UTC
[jira] [Created] (ARROW-12631) [Python] Should dataset.write_table
accept a Scanner?
Joris Van den Bossche created ARROW-12631:
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Summary: [Python] Should dataset.write_table accept a Scanner?
Key: ARROW-12631
URL: https://issues.apache.org/jira/browse/ARROW-12631
Project: Apache Arrow
Issue Type: Improvement
Components: Python
Reporter: Joris Van den Bossche
Assume you open a dataset and want to write it back with some projected columns. Currently you need to actually materialize it to a Table or convert it to an iterator of batches, for being able to write the dataset:
{code:python}
import pyarrow.dataset as ds
dataset = ds.dataset(pa.table({'a': [1, 2, 3]}))
# write with projected columns
projection = {'b': ds.field('a')}
# this works but materializes full table
ds.write_dataset(dataset.to_table(columns=projection), "test.parquet", format="parquet")
# this requires the exact schema, which is a bit annoying as you need to construct that manually
ds.write_dataset(dataset.to_batches(columns=projection), "test.parquet", format="parquet", schema=...<projected schema>...)
{code}
You could expect to do the following?
{code}
ds.write_dataset(dataset.scanner(columns=projection), "test.parquet", format="parquet")
{code}
cc [~lidavidm] do you think this logic is correct?
(encountered this while trying to reproduce ARROW-12620 in Python)
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