[jira] [Created] (ARROW-12314) [Python] pq.read_pandas with use_legacy_dataset=False does not accept columns as a set (kartothek integration failure)

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

Joris Van den Bossche created ARROW-12314:
---------------------------------------------

             Summary: [Python] pq.read_pandas with use_legacy_dataset=False does not accept columns as a set (kartothek integration failure)
                 Key: ARROW-12314
                 URL: https://issues.apache.org/jira/browse/ARROW-12314
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
            Reporter: Joris Van den Bossche
             Fix For: 4.0.0


The kartothek nightly integration builds started to fail(https://github.com/ursacomputing/crossbow/runs/2303373464), I assume because of ARROW-11464 (https://github.com/apache/arrow/pull/9910).

It seems that in the new ParquetDatasetV2 (what you get with {{use_legacy_dataset=False}}), the handling of the columns argument is slightly different.


Example failure:

{code}
_____________________ test_add_column_to_existing_index[4] _____________________

store_factory = functools.partial(<function get_store_from_url at 0x7faf12e9d0e0>, 'hfs:///tmp/pytest-of-root/pytest-0/test_add_column_to_existing_in1/store')
metadata_version = 4
bound_build_dataset_indices = <function build_dataset_indices at 0x7faf0c509830>

    def test_add_column_to_existing_index(
        store_factory, metadata_version, bound_build_dataset_indices
    ):
        dataset_uuid = "dataset_uuid"
        partitions = [
            pd.DataFrame({"p": [1, 2], "x": [100, 4500]}),
            pd.DataFrame({"p": [4, 3], "x": [500, 10]}),
        ]
    
        dataset = store_dataframes_as_dataset(
            dfs=partitions,
            store=store_factory,
            dataset_uuid=dataset_uuid,
            metadata_version=metadata_version,
            secondary_indices="p",
        )
        assert dataset.load_all_indices(store=store_factory()).indices.keys() == {"p"}
    
        # Create indices
>       bound_build_dataset_indices(store_factory, dataset_uuid, columns=["x"])

kartothek/io/testing/index.py:88: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/conda/envs/arrow/lib/python3.7/site-packages/decorator.py:231: in fun
    return caller(func, *(extras + args), **kw)
kartothek/io_components/utils.py:277: in normalize_args
    return _wrapper(*args, **kwargs)
kartothek/io_components/utils.py:275: in _wrapper
    return function(*args, **kwargs)
kartothek/io/eager.py:706: in build_dataset_indices
    mp = mp.load_dataframes(store=ds_factory.store, columns=cols_to_load,)
kartothek/io_components/metapartition.py:150: in _impl
    method_return = method(mp, *method_args, **method_kwargs)
kartothek/io_components/metapartition.py:696: in load_dataframes
    date_as_object=dates_as_object,
kartothek/serialization/_generic.py:122: in restore_dataframe
    date_as_object=date_as_object,
kartothek/serialization/_parquet.py:302: in restore_dataframe
    date_as_object=date_as_object,
kartothek/serialization/_parquet.py:249: in _restore_dataframe
    table = pq.read_pandas(reader, columns=columns)
/opt/conda/envs/arrow/lib/python3.7/site-packages/pyarrow/parquet.py:1768: in read_pandas
    source, columns=columns, use_pandas_metadata=True, **kwargs
/opt/conda/envs/arrow/lib/python3.7/site-packages/pyarrow/parquet.py:1730: in read_table
    use_pandas_metadata=use_pandas_metadata)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <pyarrow.parquet._ParquetDatasetV2 object at 0x7faee1ed9550>
columns = {'x'}, use_threads = True, use_pandas_metadata = True

    def read(self, columns=None, use_threads=True, use_pandas_metadata=False):
        """
        Read (multiple) Parquet files as a single pyarrow.Table.
    
        Parameters
        ----------
        columns : List[str]
            Names of columns to read from the dataset. The partition fields
            are not automatically included (in contrast to when setting
            ``use_legacy_dataset=True``).
        use_threads : bool, default True
            Perform multi-threaded column reads.
        use_pandas_metadata : bool, default False
            If True and file has custom pandas schema metadata, ensure that
            index columns are also loaded.
    
        Returns
        -------
        pyarrow.Table
            Content of the file as a table (of columns).
        """
        # if use_pandas_metadata, we need to include index columns in the
        # column selection, to be able to restore those in the pandas DataFrame
        metadata = self.schema.metadata
        if columns is not None and use_pandas_metadata:
            if metadata and b'pandas' in metadata:
                # RangeIndex can be represented as dict instead of column name
                index_columns = [
                    col for col in _get_pandas_index_columns(metadata)
                    if not isinstance(col, dict)
                ]
>               columns = columns + list(set(index_columns) - set(columns))
E               TypeError: unsupported operand type(s) for +: 'set' and 'list'

/opt/conda/envs/arrow/lib/python3.7/site-packages/pyarrow/parquet.py:1598: TypeError
{code}



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