[GitHub] [spark] EnricoMi commented on a diff in pull request #38624: [SPARK-40559][PYTHON] Add applyInArrow to groupBy and cogroup

["EnricoMi (via GitHub)" <gi...@apache.org>]

EnricoMi commented on code in PR #38624:
URL: https://github.com/apache/spark/pull/38624#discussion_r1202124485


##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -147,6 +147,49 @@ def wrap_and_init_stream():
         return super(ArrowStreamUDFSerializer, self).dump_stream(wrap_and_init_stream(), stream)
 
 
+class ArrowStreamGroupUDFSerializer(ArrowStreamUDFSerializer):
+    """
+    Serializes pyarrow.RecordBatch data with Arrow streaming format.
+
+    Loads Arrow record batches as `[[pa.RecordBatch]]` (one `[pa.RecordBatch]` per group)
+    and serializes `[([pa.RecordBatch], arrow_type)]`.
+
+    Parameters
+    ----------
+    assign_cols_by_name : bool
+        If True, then DataFrames will get columns by name
+    """
+
+    def __init__(self, assign_cols_by_name):
+        super(ArrowStreamGroupUDFSerializer, self).__init__()
+        self._assign_cols_by_name = assign_cols_by_name
+
+    def dump_stream(self, iterator, stream):
+        import pyarrow as pa
+
+        # flatten inner list [([pa.RecordBatch], arrow_type)] into [(pa.RecordBatch, arrow_type)]
+        # so strip off inner iterator induced by ArrowStreamUDFSerializer.load_stream
+        batch_iter = [
+            (batch, arrow_type)
+            for batches, arrow_type in iterator  # tuple constructed in wrap_grouped_map_arrow_udf
+            for batch in batches
+        ]
+
+        if self._assign_cols_by_name:
+            batch_iter = [
+                (
+                    pa.RecordBatch.from_arrays(
+                        [batch.column(field.name) for field in arrow_type],
+                        names=[field.name for field in arrow_type],
+                    ),
+                    arrow_type,
+                )
+                for batch, arrow_type in batch_iter
+            ]

Review Comment:
   Thanks for highlighting this, I have changed this to a generator.



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