[jira] [Created] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset

["Uwe L. Korn (JIRA)" <ji...@apache.org>]

Uwe L. Korn created ARROW-2659:
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             Summary: [Python] More graceful reading of empty String columns in ParquetDataset
                 Key: ARROW-2659
                 URL: https://issues.apache.org/jira/browse/ARROW-2659
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 0.9.0
            Reporter: Uwe L. Korn
             Fix For: 0.11.0


When currently saving a {{ParquetDataset}} from Pandas, we don't get consistent schemas, even if the source was a single DataFrame. This is due to the fact that in some partitions object columns like string can become empty. Then the resulting Arrow schema will differ. In the central metadata, we will store this column as {{pa.string}} whereas in the partition file with the empty columns, this columns will be stored as {{pa.null}}.

The two schemas are still a valid match in terms of schema evolution and we should respect that in https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754 Instead of doing a {{pa.Schema.equals}} in https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778 we should introduce a new method {{pa.Schema.can_evolve_to}} that is more graceful and returns {{True}} if a dataset piece has a null column where the main metadata states a nullable column of any type.



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