[SPARK-26887][SQL][PYTHON] Create datetime.date directly instead of creating datetime64[ns] as intermediate data.

python/pyspark/serializers.py

@@ -311,10 +311,9 @@ def __init__(self, timezone, safecheck):
 
     def arrow_to_pandas(self, arrow_column):
         from pyspark.sql.types import from_arrow_type, \
-            _check_series_convert_date, _check_series_localize_timestamps
+            _arrow_column_to_pandas, _check_series_localize_timestamps
 
-        s = arrow_column.to_pandas()
-        s = _check_series_convert_date(s, from_arrow_type(arrow_column.type))
+        s = _arrow_column_to_pandas(arrow_column, from_arrow_type(arrow_column.type))
         s = _check_series_localize_timestamps(s, self._timezone)
         return s
 

python/pyspark/sql/dataframe.py

@@ -2107,14 +2107,13 @@ def toPandas(self):
             # of PyArrow is found, if 'spark.sql.execution.arrow.enabled' is enabled.
             if use_arrow:
                 try:
-                    from pyspark.sql.types import _check_dataframe_convert_date, \
+                    from pyspark.sql.types import _arrow_table_to_pandas, \
                         _check_dataframe_localize_timestamps
                     import pyarrow
                     batches = self._collectAsArrow()
                     if len(batches) > 0:
                         table = pyarrow.Table.from_batches(batches)
-                        pdf = table.to_pandas()
-                        pdf = _check_dataframe_convert_date(pdf, self.schema)
+                        pdf = _arrow_table_to_pandas(table, self.schema)
                         return _check_dataframe_localize_timestamps(pdf, timezone)
                     else:
                         return pd.DataFrame.from_records([], columns=self.columns)

python/pyspark/sql/tests/test_arrow.py

@@ -68,14 +68,17 @@ def setUpClass(cls):
                     (u"b", 2, 20, 0.4, 4.0, Decimal("4.0"),
                      date(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2)),
                     (u"c", 3, 30, 0.8, 6.0, Decimal("6.0"),
-                     date(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3))]
+                     date(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3)),
+                    (u"d", 4, 40, 1.0, 8.0, Decimal("8.0"),
+                     date(2262, 4, 12), datetime(2262, 3, 3, 3, 3, 3))]
 
         # TODO: remove version check once minimum pyarrow version is 0.10.0
         if LooseVersion("0.10.0") <= LooseVersion(pa.__version__):
             cls.schema.add(StructField("9_binary_t", BinaryType(), True))
             cls.data[0] = cls.data[0] + (bytearray(b"a"),)
             cls.data[1] = cls.data[1] + (bytearray(b"bb"),)
             cls.data[2] = cls.data[2] + (bytearray(b"ccc"),)
+            cls.data[3] = cls.data[3] + (bytearray(b"dddd"),)
 
     @classmethod
     def tearDownClass(cls):

python/pyspark/sql/tests/test_pandas_udf_scalar.py

@@ -349,7 +349,8 @@ def test_vectorized_udf_dates(self):
         data = [(0, date(1969, 1, 1),),
                 (1, date(2012, 2, 2),),
                 (2, None,),
-                (3, date(2100, 4, 4),)]
+                (3, date(2100, 4, 4),),
+                (4, date(2262, 4, 12),)]
         df = self.spark.createDataFrame(data, schema=schema)
 
         date_copy = pandas_udf(lambda t: t, returnType=DateType())

python/pyspark/sql/types.py

@@ -1681,38 +1681,53 @@ def from_arrow_schema(arrow_schema):
          for field in arrow_schema])
 
 
-def _check_series_convert_date(series, data_type):
-    """
-    Cast the series to datetime.date if it's a date type, otherwise returns the original series.
+def _arrow_column_to_pandas(column, data_type):
+    """ Convert Arrow Column to pandas Series.
+
+    If the given column is a date type column, creates a series of datetime.date directly instead
+    of creating datetime64[ns] as intermediate data.
 
-    :param series: pandas.Series
-    :param data_type: a Spark data type for the series
+    :param series: pyarrow.lib.Column
+    :param data_type: a Spark data type for the column
     """
-    import pyarrow
+    import pandas as pd
+    import pyarrow as pa
     from distutils.version import LooseVersion
-    # As of Arrow 0.12.0, date_as_objects is True by default, see ARROW-3910
-    if LooseVersion(pyarrow.__version__) < LooseVersion("0.12.0") and type(data_type) == DateType:
-        return series.dt.date
+    # Since Arrow 0.11.0, support date_as_object to return datetime.date instead of np.datetime64.
+    if LooseVersion(pa.__version__) < LooseVersion("0.11.0"):
+        if type(data_type) == DateType:
+            return pd.Series(column.to_pylist(), name=column.name)
+        else:
+            return column.to_pandas()
     else:
-        return series
+        return column.to_pandas(date_as_object=True)
+
 
+def _arrow_table_to_pandas(table, schema):
+    """ Convert Arrow Table to pandas DataFrame.
 
-def _check_dataframe_convert_date(pdf, schema):
-    """ Correct date type value to use datetime.date.
+    If the given table contains a date type column, use `_arrow_column_to_pandas` for pyarrow<0.11
+    or use `date_as_object` option for pyarrow>=0.11 to avoid creating datetime64[ns] as
+    intermediate data.
 
     Pandas DataFrame created from PyArrow uses datetime64[ns] for date type values, but we should
     use datetime.date to match the behavior with when Arrow optimization is disabled.
 
-    :param pdf: pandas.DataFrame
-    :param schema: a Spark schema of the pandas.DataFrame
+    :param table: pyarrow.lib.Table
+    :param schema: a Spark schema of the pyarrow.lib.Table
     """
-    import pyarrow
+    import pandas as pd
+    import pyarrow as pa
     from distutils.version import LooseVersion
-    # As of Arrow 0.12.0, date_as_objects is True by default, see ARROW-3910
-    if LooseVersion(pyarrow.__version__) < LooseVersion("0.12.0"):
-        for field in schema:
-            pdf[field.name] = _check_series_convert_date(pdf[field.name], field.dataType)
-    return pdf
+    # Since Arrow 0.11.0, support date_as_object to return datetime.date instead of np.datetime64.
+    if LooseVersion(pa.__version__) < LooseVersion("0.11.0"):
+        if any(type(field.dataType) == DateType for field in schema):
+            return pd.concat([_arrow_column_to_pandas(column, field.dataType)
+                              for column, field in zip(table.itercolumns(), schema)], axis=1)
+        else:
+            return table.to_pandas()
+    else:
+        return table.to_pandas(date_as_object=True)
 
 
 def _get_local_timezone():