[jira] [Assigned] (SPARK-42021) createDataFrame with array.array

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-42021?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-42021:
------------------------------------

    Assignee: Apache Spark

> createDataFrame with array.array
> --------------------------------
>
>                 Key: SPARK-42021
>                 URL: https://issues.apache.org/jira/browse/SPARK-42021
>             Project: Spark
>          Issue Type: Sub-task
>          Components: Connect
>    Affects Versions: 3.4.0
>            Reporter: Hyukjin Kwon
>            Assignee: Apache Spark
>            Priority: Major
>
> {code}
> pyspark/sql/tests/test_types.py:964 (TypesParityTests.test_array_types)
> self = <pyspark.sql.tests.connect.test_parity_types.TypesParityTests testMethod=test_array_types>
>     def test_array_types(self):
>         # This test need to make sure that the Scala type selected is at least
>         # as large as the python's types. This is necessary because python's
>         # array types depend on C implementation on the machine. Therefore there
>         # is no machine independent correspondence between python's array types
>         # and Scala types.
>         # See: https://docs.python.org/2/library/array.html
>     
>         def assertCollectSuccess(typecode, value):
>             row = Row(myarray=array.array(typecode, [value]))
>             df = self.spark.createDataFrame([row])
>             self.assertEqual(df.first()["myarray"][0], value)
>     
>         # supported string types
>         #
>         # String types in python's array are "u" for Py_UNICODE and "c" for char.
>         # "u" will be removed in python 4, and "c" is not supported in python 3.
>         supported_string_types = []
>         if sys.version_info[0] < 4:
>             supported_string_types += ["u"]
>             # test unicode
> >           assertCollectSuccess("u", "a")
> ../test_types.py:986: 
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
> ../test_types.py:975: in assertCollectSuccess
>     df = self.spark.createDataFrame([row])
> ../../connect/session.py:278: in createDataFrame
>     _table = pa.Table.from_pylist([row.asDict(recursive=True) for row in _data])
> pyarrow/table.pxi:3700: in pyarrow.lib.Table.from_pylist
>     ???
> pyarrow/table.pxi:5221: in pyarrow.lib._from_pylist
>     ???
> pyarrow/table.pxi:3575: in pyarrow.lib.Table.from_arrays
>     ???
> pyarrow/table.pxi:1383: in pyarrow.lib._sanitize_arrays
>     ???
> pyarrow/table.pxi:1364: in pyarrow.lib._schema_from_arrays
>     ???
> pyarrow/array.pxi:320: in pyarrow.lib.array
>     ???
> pyarrow/array.pxi:39: in pyarrow.lib._sequence_to_array
>     ???
> pyarrow/error.pxi:144: in pyarrow.lib.pyarrow_internal_check_status
>     ???
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
> >   ???
> E   pyarrow.lib.ArrowInvalid: Could not convert array('u', 'a') with type array.array: did not recognize Python value type when inferring an Arrow data type
> pyarrow/error.pxi:100: ArrowInvalid
> {code}



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