[SPARK-45143][PYTHON][CONNECT] Make PySpark compatible with PyArrow 13.0.0

.github/workflows/build_and_test.yml

@@ -263,7 +263,7 @@ jobs:
     - name: Install Python packages (Python 3.8)
       if: (contains(matrix.modules, 'sql') && !contains(matrix.modules, 'sql-')) || contains(matrix.modules, 'connect')
       run: |
-        python3.8 -m pip install 'numpy>=1.20.0' 'pyarrow==12.0.1' pandas scipy unittest-xml-reporting 'grpcio>=1.48,<1.57' 'grpcio-status>=1.48,<1.57' 'protobuf==3.20.3'
+        python3.8 -m pip install 'numpy>=1.20.0' pyarrow pandas scipy unittest-xml-reporting 'grpcio>=1.48,<1.57' 'grpcio-status>=1.48,<1.57' 'protobuf==3.20.3'
         python3.8 -m pip list
     # Run the tests.
     - name: Run tests
@@ -728,7 +728,7 @@ jobs:
         #   See also https://issues.apache.org/jira/browse/SPARK-38279.
         python3.9 -m pip install 'sphinx<3.1.0' mkdocs pydata_sphinx_theme nbsphinx numpydoc 'jinja2<3.0.0' 'markupsafe==2.0.1' 'pyzmq<24.0.0'
         python3.9 -m pip install ipython_genutils # See SPARK-38517
-        python3.9 -m pip install sphinx_plotly_directive 'numpy>=1.20.0' 'pyarrow==12.0.1' pandas 'plotly>=4.8'
+        python3.9 -m pip install sphinx_plotly_directive 'numpy>=1.20.0' pyarrow pandas 'plotly>=4.8'
         python3.9 -m pip install 'docutils<0.18.0' # See SPARK-39421
         apt-get update -y
         apt-get install -y ruby ruby-dev

dev/infra/Dockerfile

@@ -85,7 +85,7 @@ RUN Rscript -e "devtools::install_version('roxygen2', version='7.2.0', repos='ht
 ENV R_LIBS_SITE "/usr/local/lib/R/site-library:${R_LIBS_SITE}:/usr/lib/R/library"
 
 RUN pypy3 -m pip install numpy 'pandas<=2.0.3' scipy coverage matplotlib
-RUN python3.9 -m pip install numpy 'pyarrow==12.0.1' 'pandas<=2.0.3' scipy unittest-xml-reporting plotly>=4.8 'mlflow>=2.3.1' coverage matplotlib openpyxl 'memory-profiler==0.60.0' 'scikit-learn==1.1.*'
+RUN python3.9 -m pip install numpy pyarrow 'pandas<=2.0.3' scipy unittest-xml-reporting plotly>=4.8 'mlflow>=2.3.1' coverage matplotlib openpyxl 'memory-profiler==0.60.0' 'scikit-learn==1.1.*'
 
 # Add Python deps for Spark Connect.
 RUN python3.9 -m pip install 'grpcio>=1.48,<1.57' 'grpcio-status>=1.48,<1.57' 'protobuf==3.20.3' 'googleapis-common-protos==1.56.4'

python/pyspark/pandas/typedef/typehints.py

@@ -293,7 +293,9 @@ def spark_type_to_pandas_dtype(
         ),
     ):
         return np.dtype("object")
-    elif isinstance(spark_type, types.TimestampType):
+    elif isinstance(spark_type, types.DayTimeIntervalType):
+        return np.dtype("timedelta64[ns]")
+    elif isinstance(spark_type, (types.TimestampType, types.TimestampNTZType)):
         return np.dtype("datetime64[ns]")
     else:
         return np.dtype(to_arrow_type(spark_type).to_pandas_dtype())

python/pyspark/sql/connect/client/core.py

@@ -33,6 +33,7 @@
 import urllib.parse
 import uuid
 import sys
+from distutils.version import LooseVersion
 from types import TracebackType
 from typing import (
     Iterable,
@@ -880,19 +881,30 @@ def to_pandas(self, plan: pb2.Plan) -> "pd.DataFrame":
 
         # Rename columns to avoid duplicated column names.
         renamed_table = table.rename_columns([f"col_{i}" for i in range(table.num_columns)])
+
+        pandas_options = {}
         if self_destruct:
             # Configure PyArrow to use as little memory as possible:
             # self_destruct - free columns as they are converted
             # split_blocks - create a separate Pandas block for each column
             # use_threads - convert one column at a time
-            pandas_options = {
-                "self_destruct": True,
-                "split_blocks": True,
-                "use_threads": False,
-            }
-            pdf = renamed_table.to_pandas(**pandas_options)
-        else:
-            pdf = renamed_table.to_pandas()
+            pandas_options.update(
+                {
+                    "self_destruct": True,
+                    "split_blocks": True,
+                    "use_threads": False,
+                }
+            )
+        if LooseVersion(pa.__version__) >= LooseVersion("13.0.0"):
+            # A legacy option to coerce date32, date64, duration, and timestamp
+            # time units to nanoseconds when converting to pandas.
+            # This option can only be added since 13.0.0.
+            pandas_options.update(
+                {
+                    "coerce_temporal_nanoseconds": True,
+                }
+            )
+        pdf = renamed_table.to_pandas(**pandas_options)
         pdf.columns = schema.names
 
         if len(pdf.columns) > 0:

python/pyspark/sql/pandas/conversion.py

@@ -26,6 +26,7 @@
     TYPE_CHECKING,
 )
 from warnings import warn
+from distutils.version import LooseVersion
 
 from pyspark.errors.exceptions.captured import unwrap_spark_exception
 from pyspark.rdd import _load_from_socket
@@ -125,19 +126,30 @@ def toPandas(self) -> "PandasDataFrameLike":
             # of PyArrow is found, if 'spark.sql.execution.arrow.pyspark.enabled' is enabled.
             if use_arrow:
                 try:
-                    import pyarrow
+                    import pyarrow as pa
 
                     self_destruct = jconf.arrowPySparkSelfDestructEnabled()
                     batches = self._collect_as_arrow(split_batches=self_destruct)
                     if len(batches) > 0:
-                        table = pyarrow.Table.from_batches(batches)
+                        table = pa.Table.from_batches(batches)
                         # Ensure only the table has a reference to the batches, so that
                         # self_destruct (if enabled) is effective
                         del batches
                         # 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.
                         pandas_options = {"date_as_object": True}
+
+                        if LooseVersion(pa.__version__) >= LooseVersion("13.0.0"):
+                            # A legacy option to coerce date32, date64, duration, and timestamp
+                            # time units to nanoseconds when converting to pandas.
+                            # This option can only be added since 13.0.0.
+                            pandas_options.update(
+                                {
+                                    "coerce_temporal_nanoseconds": True,
+                                }
+                            )
+
                         if self_destruct:
                             # Configure PyArrow to use as little memory as possible:
                             # self_destruct - free columns as they are converted

python/pyspark/sql/pandas/serializers.py

@@ -185,7 +185,22 @@ def arrow_to_pandas(self, arrow_column, struct_in_pandas="dict", ndarray_as_list
         # instead of creating datetime64[ns] as intermediate data to avoid overflow caused by
         # datetime64[ns] type handling.
         # Cast dates to objects instead of datetime64[ns] dtype to avoid overflow.
-        s = arrow_column.to_pandas(date_as_object=True)
+        pandas_options = {"date_as_object": True}
+
+        import pyarrow as pa
+        from distutils.version import LooseVersion
+
+        if LooseVersion(pa.__version__) >= LooseVersion("13.0.0"):
+            # A legacy option to coerce date32, date64, duration, and timestamp
+            # time units to nanoseconds when converting to pandas.
+            # This option can only be added since 13.0.0.
+            pandas_options.update(
+                {
+                    "coerce_temporal_nanoseconds": True,
+                }
+            )
+
+        s = arrow_column.to_pandas(**pandas_options)
 
         # TODO(SPARK-43579): cache the converter for reuse
         converter = _create_converter_to_pandas(