dianfu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r831004269
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -1174,6 +1174,66 @@ 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 = 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 = 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 :class:`pyflink.common.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 of int or str type to locate
the value to sum")
+
+ class SumReduceFunction(ReduceFunction):
+
+ def __init__(self, position_to_sum):
+ self._pos = position_to_sum
+
+ def reduce(self, value1, value2):
+ from numbers import Number
+ if not isinstance(value1[self._pos], Number):
Review comment:
What happens when the first element is a number and the second element
is not a number?
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