dianfu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829715015
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -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)],
Review comment:
```suggestion
>>> ds = env.from_collection([('a', 1), ('a', 2), ('a', 3),
('b', 1), ('b', 2)],
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -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.
Review comment:
```suggestion
This is applicable to Tuple types, and
:class:`pyflink.common.Row` types.
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -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:
What about creating a ReduceFunction and then calling self.reduce? It
could simple the implementation.
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -455,6 +455,56 @@ def filter(self, value):
expected = ['+I[c, 1]', '+I[e, 2]']
self.assert_equals_sorted(expected, results)
+ def test_keyed_sum_with_tuple_type(self):
+ ds = self.env.from_collection([('a', 1), ('a', 2), ('a', 3), ('b', 1),
('b', 2)],
+ type_info=Types.TUPLE([Types.STRING(),
Types.INT()]))
+ keyed_stream = ds.key_by(lambda x: x[0], key_type=Types.STRING())
+
+ keyed_stream.sum(1)\
+ .add_sink(self.test_sink)
+ self.env.execute('key_by_sum_test_with_tuple_type')
+ results = self.test_sink.get_results(False)
+ if self.__class__ == StreamingModeDataStreamTests:
Review comment:
What about splitting the test cases into StreamingModeDataStreamTests
and BatchModeDataStreamTests separately and then we could avoid this check.
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -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")
Review comment:
```suggestion
raise TypeError("The input must be of of int or str type to
locate the value to sum")
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -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(
Review comment:
```suggestion
>>> ds = env.from_collection(
```
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