[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream

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

javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829785324



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File path: flink-python/pyflink/datastream/data_stream.py
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@@ -1168,6 +1168,96 @@ def process_element(self, value, ctx: 'KeyedProcessFunction.Context'):
         return self.process(FilterKeyedProcessFunctionAdapter(func), self._original_data_type_info)\
             .name("Filter")
 
+    def sum(self, position_to_sum: Union[int, str]) -> 'DataStream':
+        """
+        Applies an aggregation that gives a rolling sum of the data stream at the
+        given position grouped by the given key. An independent aggregate is kept
+        per key.
+
+        Example(Tuple data to sum):
+        ::
+
+            >>> ds = env.from_collection([('a', 1), ('a', 2), ('b', 1), ('b', 5)])
+            >>> ds.key_by(lambda x: x[0]).sum(1)
+
+        Example(Row data to sum):
+        ::
+
+            >>> ds = self.env.from_collection([('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2)],
+            ...                                type_info=Types.ROW([Types.STRING(), Types.INT()]))
+            >>> ds.key_by(lambda x: x[0]).sum(1)
+
+        Example(Row data with fields name to sum):
+        ::
+
+            >>> ds = self.env.from_collection(
+            ...     [('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2)],
+            ...     type_info=Types.ROW_NAMED(["key", "value"], [Types.STRING(), Types.INT()])
+            ... )
+            >>> ds.key_by(lambda x: x[0]).sum("value")
+
+        :param position_to_sum:
+            The field position in the data points to sum, type can be int or str.
+            This is applicable to Tuple types, and {pyflink.common.types.Row} types.
+        :return: The transformed DataStream.
+        """
+        if not isinstance(position_to_sum, int) and not isinstance(position_to_sum, str):
+            raise TypeError("The input must be a int or str type for locate the value to sum")
+
+        output_type = _from_java_type(self._original_data_type_info.get_java_type_info())
+
+        class SumKeyedProcessFunctionAdapter(KeyedProcessFunction):

Review comment:
       Its a good idea
   By logic, Apply reduce as the underlying implementation of sum operation is good design i think.
   I'll try to apply ReduceFunction from the new design to see if it's easier to implement.
   
   




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