[jira] [Assigned] (SPARK-42021) createDataFrame with array.array
[
https://issues.apache.org/jira/browse/SPARK-42021?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon reassigned SPARK-42021:
Assignee: Hyukjin Kwon
> 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: Hyukjin Kwon
>Priority: Major
> Fix For: 3.4.0
>
>
> {code}
> pyspark/sql/tests/test_types.py:964 (TypesParityTests.test_array_types)
> self = 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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[jira] [Assigned] (SPARK-42021) createDataFrame with array.array
[
https://issues.apache.org/jira/browse/SPARK-42021?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon reassigned SPARK-42021:
Assignee: Ruifeng Zheng (was: Hyukjin Kwon)
> 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: Ruifeng Zheng
>Priority: Major
> Fix For: 3.4.0
>
>
> {code}
> pyspark/sql/tests/test_types.py:964 (TypesParityTests.test_array_types)
> self = 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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[jira] [Assigned] (SPARK-42021) createDataFrame with array.array
[
https://issues.apache.org/jira/browse/SPARK-42021?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-42021:
Assignee: (was: 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
>Priority: Major
>
> {code}
> pyspark/sql/tests/test_types.py:964 (TypesParityTests.test_array_types)
> self = 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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[jira] [Assigned] (SPARK-42021) createDataFrame with array.array
[
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 = 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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