[SPARK-51112][CONNECT] Avoid using pyarrow's to_pandas on an empty …

…table

### What changes were proposed in this pull request?

When the `pyarrow` table is empty, avoid calling the `to_pandas` method due to potential segfault failures. Instead, an empty pandas dataframe is created manually.

### Why are the changes needed?

Consider the following code:
```python
from pyspark.sql.types import StructField, ArrayType, StringType, StructType, IntegerType
import faulthandler
faulthandler.enable()
spark = SparkSession.builder \
    .remote("sc://localhost:15002") \
    .getOrCreate()
sp_df = spark.createDataFrame(
    data = [],
    schema=StructType(
        [
            StructField(
                name='b_int',
                dataType=IntegerType(),
                nullable=False,
            ),
            StructField(
                name='b',
                dataType=ArrayType(ArrayType(StringType(), True), True),
                nullable=True,
            ),
        ]
    )
)
print(sp_df)
print('Spark dataframe generated.')
print(sp_df.toPandas())
print('Pandas dataframe generated.')
```
Executing this may lead to a segfault when the line `sp_df.toPandas()` is run.
Example:
```
Thread 0x00000001f1904f40 (most recent call first):
  File "/Users/venkata.gudesa/spark/test_env/lib/python3.13/site-packages/pyarrow/pandas_compat.py", line 808 in table_to_dataframe
  File "/Users/venkata.gudesa/spark/test_env/lib/python3.13/site-packages/pyspark/sql/connect/client/core.py", line 949 in to_pandas
  File "/Users/venkata.gudesa/spark/test_env/lib/python3.13/site-packages/pyspark/sql/connect/dataframe.py", line 1857 in toPandas
  File "<python-input-3>", line 1 in <module>
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/code.py", line 92 in runcode
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/_pyrepl/console.py", line 205 in runsource
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/code.py", line 313 in push
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/_pyrepl/simple_interact.py", line 160 in run_multiline_interactive_console
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/_pyrepl/main.py", line 59 in interactive_console
  File "/opt/homebrew/Cellar/python3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/_pyrepl/__main__.py", line 6 in <module>
  File "<frozen runpy>", line 88 in _run_code
```

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

New unit test.

### Was this patch authored or co-authored using generative AI tooling?

No.

Closes apache#49834 from vicennial/SPARK-51112.

Lead-authored-by: vicennial <venkata.gudesa@databricks.com>
Co-authored-by: Venkata Sai Akhil Gudesa <gvs.akhil1997@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

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

@@ -933,33 +933,39 @@ def to_pandas(
         schema = schema or from_arrow_schema(table.schema, prefer_timestamp_ntz=True)
         assert schema is not None and isinstance(schema, StructType)
 
-        # 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.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
+        # SPARK-51112: If the table is empty, we avoid using pyarrow to_pandas to create the
+        # DataFrame, as it may fail with a segmentation fault. Instead, we create an empty pandas
+        # DataFrame manually with the correct schema.
+        if table.num_rows == 0:
+            pdf = pd.DataFrame(columns=schema.names)
+        else:
+            # 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.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:
             timezone: Optional[str] = None

python/pyspark/sql/tests/connect/test_parity_collection.py

@@ -42,6 +42,9 @@ def test_to_pandas_with_duplicated_column_names(self):
     def test_to_pandas_from_mixed_dataframe(self):
         self.check_to_pandas_from_mixed_dataframe()
 
+    def test_to_pandas_for_empty_df_with_nested_array_columns(self):
+        self.check_to_pandas_for_empty_df_with_nested_array_columns()
+
 
 if __name__ == "__main__":
     import unittest

python/pyspark/sql/tests/test_collection.py

@@ -18,9 +18,11 @@
 import unittest
 
 from pyspark.sql.types import (
+    ArrayType,
     StringType,
     IntegerType,
     StructType,
+    StructField,
     BooleanType,
     DateType,
     TimestampType,
@@ -35,6 +37,7 @@
     pandas_requirement_message,
     pyarrow_requirement_message,
 )
+from pyspark.testing.utils import assertDataFrameEqual
 
 
 class DataFrameCollectionTestsMixin:
@@ -289,6 +292,40 @@ def check_to_pandas_for_array_of_struct(self, is_arrow_enabled):
         else:
             self.assertEqual(type(pdf["array_struct_col"][0]), list)
 
+    @unittest.skipIf(
+        not have_pandas or not have_pyarrow,
+        pandas_requirement_message or pyarrow_requirement_message,
+    )
+    def test_to_pandas_for_empty_df_with_nested_array_columns(self):
+        for arrow_enabled in [False, True]:
+            with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": arrow_enabled}):
+                self.check_to_pandas_for_empty_df_with_nested_array_columns()
+
+    def check_to_pandas_for_empty_df_with_nested_array_columns(self):
+        # SPARK-51112: Segfault must not occur when converting empty DataFrame with nested array
+        # columns to pandas DataFrame.
+        import pandas as pd
+
+        df = self.spark.createDataFrame(
+            data=[],
+            schema=StructType(
+                [
+                    StructField(
+                        name="b_int",
+                        dataType=IntegerType(),
+                        nullable=False,
+                    ),
+                    StructField(
+                        name="b",
+                        dataType=ArrayType(ArrayType(StringType(), True), True),
+                        nullable=True,
+                    ),
+                ]
+            ),
+        )
+        expected_pdf = pd.DataFrame(columns=["b_int", "b"])
+        assertDataFrameEqual(df.toPandas(), expected_pdf)
+
     def test_to_local_iterator(self):
         df = self.spark.range(8, numPartitions=4)
         expected = df.collect()