[GitHub] [spark] viirya commented on a change in pull request #24177: [SPARK-27240][PYTHON] Use pandas DataFrame for struct type argument in Scalar Pandas UDF.

[GitBox <gi...@apache.org>]

viirya commented on a change in pull request #24177: [SPARK-27240][PYTHON] Use pandas DataFrame for struct type argument in Scalar Pandas UDF.
URL: https://github.com/apache/spark/pull/24177#discussion_r268101587
 
 

 ##########
 File path: python/pyspark/serializers.py
 ##########
 @@ -378,6 +379,29 @@ class ArrowStreamPandasUDFSerializer(ArrowStreamPandasSerializer):
     Serializer used by Python worker to evaluate Pandas UDFs
     """
 
+    def __init__(self, timezone, safecheck, assign_cols_by_name, df_for_struct=False):
+        super(ArrowStreamPandasUDFSerializer, self) \
+            .__init__(timezone, safecheck, assign_cols_by_name)
+        self._df_for_struct = df_for_struct
+
+    def arrow_to_pandas(self, arrow_column, data_type):
+        from pyspark.sql.types import StructType, \
+            _arrow_column_to_pandas, _check_dataframe_localize_timestamps
+
+        if self._df_for_struct and type(data_type) == StructType:
+            import pandas as pd
+            import pyarrow as pa
+            column_arrays = zip(*[[chunk.field(i)
+                                   for i in range(chunk.type.num_children)]
+                                  for chunk in arrow_column.data.iterchunks()])
+            series = [_arrow_column_to_pandas(pa.column(field.name, pa.chunked_array(arrays)),
+                                              field.dataType)
+                      for arrays, field in zip(column_arrays, data_type)]
+            s = _check_dataframe_localize_timestamps(pd.concat(series, axis=1), self._timezone)
+        else:
+            s = super(ArrowStreamPandasUDFSerializer, self).arrow_to_pandas(arrow_column, data_type)
 
 Review comment:
   Will this create a new serializer each time calling `arrow_to_pandas`?

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