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https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16498163#comment-16498163
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Aldrin commented on ARROW-2659:
-------------------------------
In trying to read from a parquet dataset using validate_schema=False, I then
get the following exception (see
read_parquet_dataset.error.read_table.novalidation.txt):
{noformat}
Traceback (most recent call last):
File "toolbox/read_parquet.py", line 85, in <module>
dsv_data_table = read_parquet_data(parsed_args.input_path)
File "toolbox/read_parquet.py", line 64, in read_parquet_data
use_pandas_metadata=use_pandas_metadata
File
"/Users/amontana/.local/share/virtualenvs/kitsi-zdrw075I/lib/python3.6/site-packages/pyarrow/parquet.py",
line 806, in read
all_data = lib.concat_tables(tables)
File "table.pxi", line 1285, in pyarrow.lib.concat_tables
File "error.pxi", line 77, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 3 was different:{noformat}
Given this error, it seems that validating the schema is just one piece of the
overall difficulty, and some way of pushing down the valid schema evolution
into concat_tables in table.pxi will also be necessary.
[~xhochy], I'm not sure if this impression is correct, and if it is, whether we
would want a related Jira to track changes in (or underlying) the concat_tables
function.
> [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
> Priority: Major
> Labels: beginner
> Fix For: 0.11.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> 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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