[SPARK-26887][SQL][PYTHON][NS] Create datetime.date directly instead …

…of creating datetime64 as intermediate data.

## What changes were proposed in this pull request?

Currently `DataFrame.toPandas()` with arrow enabled or `ArrowStreamPandasSerializer` for pandas UDF with pyarrow<0.12 creates `datetime64[ns]` type series as intermediate data and then convert to `datetime.date` series, but the intermediate `datetime64[ns]` might cause an overflow even if the date is valid.

```
>>> import datetime
>>>
>>> t = [datetime.date(2262, 4, 12), datetime.date(2263, 4, 12)]
>>>
>>> df = spark.createDataFrame(t, 'date')
>>> df.show()
+----------+
|     value|
+----------+
|2262-04-12|
|2263-04-12|
+----------+

>>>
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>>
>>> df.toPandas()
        value
0  1677-09-21
1  1678-09-21
```

We should avoid creating such intermediate data and create `datetime.date` series directly instead.

## How was this patch tested?

Modified some tests to include the date which overflow caused by the intermediate conversion.
Run tests with pyarrow 0.8, 0.10, 0.11, 0.12 in my local environment.

Closes apache#23795 from ueshin/issues/SPARK-26887/date_as_object.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

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 +1681,52 @@ 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.
 
-    :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
+    # If the given column is a date type column, creates a series of datetime.date directly instead
+    # of creating datetime64[ns] as intermediate data to avoid overflow caused by datetime64[ns]
+    # type handling.
+    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
+        # Since Arrow 0.11.0, support date_as_object to return datetime.date instead of
+        # np.datetime64.
+        return column.to_pandas(date_as_object=True)
 
 
-def _check_dataframe_convert_date(pdf, schema):
-    """ Correct date type value to use datetime.date.
+def _arrow_table_to_pandas(table, schema):
+    """ Convert Arrow Table to pandas DataFrame.
 
     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
+    # 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.
+    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():