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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/03/17 06:53:29 UTC
[GitHub] [flink] javacaoyu opened a new pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
javacaoyu opened a new pull request #19126:
URL: https://github.com/apache/flink/pull/19126
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[GitHub] [flink] dianfu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
dianfu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1072215788
> When finish this PR, I can try to implement one of the methods first if you assign the jira task to me.
Could you create the JIRA? Then I can assign it to you~
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] javacaoyu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1072352795
Hi @dianfu
I have already finished that problem you raised.
Wait you have time to check it and give it suggests.
-----------------------------
And the JIRA task are created.
[FLINK-26728](https://issues.apache.org/jira/browse/FLINK-26728) min
[FLINK-26729](https://issues.apache.org/jira/browse/FLINK-26729) min_by
[FLINK-26730](https://issues.apache.org/jira/browse/FLINK-26730) max
[FLINK-26731](https://issues.apache.org/jira/browse/FLINK-26731) max_by
Thanks.
Best
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] dianfu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
dianfu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1072215788
> When finish this PR, I can try to implement one of the methods first if you assign the jira task to me.
Could you create the JIRA? Then I can assign it to you~
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] dianfu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [flink] dianfu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r830716015
##########
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):
Review comment:
What about refactoring it as following?
```
def init_reduce_func(value1):
if isinstance(value1, tuple):
def reduce_func(v1, v2):
v1_list = list(v1)
v1_list[self._pos] = v1[self._pos] + v2[self._pos]
return tuple(v1_list)
self._reduce_func = reduce_func
elif isinstance(value1, (list, Row)):
def reduce_func(v1, v2):
v1[self._pos] = v1[self._pos] + v2[self._pos]
self._reduce_func = reduce_func
else:
raise TypeError("Sum operator only process the data of "
"Tuple type and {pyflink.common.types.Row} type. "
f"Actual type: {type(value1)}")
from numbers import Number
if not isinstance(value1[self._pos], Number):
raise TypeError("The value to sum by given position must be of numeric type; "
f"actual {type(value1[self._pos])}, expected Number")
if not self._reduce_func:
init_reduce_func(value1)
return self._reduce_func(value1, value2)
```
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829826142
##########
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:
> > When finish this PR, I can try to implement one of the methods first if you assign the jira task to me.
> > Could you create the JIRA? Then I can assign it to you~
Yes, I have already created 4 JIRA task.
list it:
- FLINK-26728 min
- FLINK-26729 min_by
- FLINK-26730 max
- FLINK-26731 max_by
Thanks.
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829785324
##########
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:
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.
##########
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:
> > When finish this PR, I can try to implement one of the methods first if you assign the jira task to me.
> > Could you create the JIRA? Then I can assign it to you~
Yes, I have already created 4 JIRA task.
list it:
- FLINK-26728 min
- FLINK-26729 min_by
- FLINK-26730 max
- FLINK-26731 max_by
Thanks.
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[GitHub] [flink] javacaoyu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1072148169
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[GitHub] [flink] dianfu closed pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
dianfu closed pull request #19126:
URL: https://github.com/apache/flink/pull/19126
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r830864455
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -1072,6 +1072,44 @@ def test_reduce_with_state(self):
expected = ['+I[a, 0]', '+I[ab, 0]', '+I[c, 1]', '+I[cd, 1]', '+I[cde, 1]']
self.assert_equals_sorted(expected, results)
+ def test_keyed_sum_with_tuple_type(self):
Review comment:
Yes, I didn't take that into account and I'm making improvements now.
Thanks.
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r830849153
##########
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):
+ raise TypeError("The value to sum by given position must be of numeric type; "
+ f"actual {type(value1[self._pos])}, expected Number")
+ if isinstance(value1, tuple):
Review comment:
Yes, list type need to be supported.
The list type was supported in the oldest code. Later I thought that because the elements in the standard list type are all of the same type.
For this kind of list data: ['key', 1] , its not a standard list to use.
Although we can key by data[0], sum by data[1]. But for this data, using tuple may be better.
So i delete the list type support.
But, By your prompts, I think the choice should be left to the user.
Whether the list used by the user is standard or not, that is the user's business.
We just need to provide list type support.
So, i will added the list type support.
and thanks for your prompts.
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r830861205
##########
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:
Thanks for your advice.
I think there's no difference between using value1 or value2.
When we got single data input(The first input value), the reduce method will not be called and its will assign the first value to reduce_state.
When we got 2 or more data, the reduce method is executed.
So, when the second data comes in, it's when the reduce method executes.
But I don't know what your considerations are, based on your considerations, would you to give some advice to me?
Thanks
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] dianfu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] javacaoyu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1075892181
Hi @dianfu
I have completed the modifications for this batch and test passed by pytest flake8 etc.
The main modifications are:
1. Modify syntax errors in comments and Exception messages.
2. Check that the value type is changed from value1 to value2.
3. Based on your suggestions, refactor the implementation of the SumReduceFunction class.
4. Support for array (list) types and basic data types (number).
5. Set the default value of 0 for the incoming parameter.
6. Merge unit test code, combining multiple test cases into one env.execute.
When you have time, please check it out and give suggestions.
Thanks a lot.
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r831017538
##########
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:
Yes, very thanks for your advice, let me be enlightened.
Sure, i only considered the normal situation, forget that when the user is using it, there is any situation.
And let me know, in any situation, check the new input always better than check the aggregated values, even that get same result in most situation.
I will to fixed that bug.
and very thanks to you, its let me programming skills up.
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[GitHub] [flink] dianfu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r830706322
##########
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):
+ raise TypeError("The value to sum by given position must be of numeric type; "
+ f"actual {type(value1[self._pos])}, expected Number")
+ if isinstance(value1, tuple):
+ value1_list = list(value1)
+ value1_list[self._pos] = value1[self._pos] + value2[self._pos]
+ value1 = tuple(value1_list)
+ elif isinstance(value1, Row):
+ value1[self._pos] = value1[self._pos] + value2[self._pos]
+ else:
+ raise TypeError("Sum operator only process the data of "
+ "Tuple type and {pyflink.common.types.Row} type. "
Review comment:
```suggestion
"Tuple type and Row type. "
```
##########
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):
+ raise TypeError("The value to sum by given position must be of numeric type; "
+ f"actual {type(value1[self._pos])}, expected Number")
+ if isinstance(value1, tuple):
Review comment:
Is is possible that the input type is a list?
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -1072,6 +1072,44 @@ def test_reduce_with_state(self):
expected = ['+I[a, 0]', '+I[ab, 0]', '+I[c, 1]', '+I[cd, 1]', '+I[cde, 1]']
self.assert_equals_sorted(expected, results)
+ def test_keyed_sum_with_tuple_type(self):
Review comment:
What about merge the added test cases into one, e.g. sum(xxx).sum(yyy).sum(zzz)? IT case is expensive and we should try to reduce them as much as possible.
##########
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:
I guess you should check value2 which is the input element?
##########
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")
Review comment:
```suggestion
raise TypeError("The field position must be of int or str type to locate the value to sum")
```
##########
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):
+ raise TypeError("The value to sum by given position must be of numeric type; "
+ f"actual {type(value1[self._pos])}, expected Number")
+ if isinstance(value1, tuple):
+ value1_list = list(value1)
+ value1_list[self._pos] = value1[self._pos] + value2[self._pos]
+ value1 = tuple(value1_list)
+ elif isinstance(value1, Row):
+ value1[self._pos] = value1[self._pos] + value2[self._pos]
+ else:
Review comment:
In the Java implementation, it also supports the following cases:
1) the input type is array
2) the input type is a basic type and the position is 0
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[GitHub] [flink] javacaoyu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on a change in pull request #19126:
URL: https://github.com/apache/flink/pull/19126#discussion_r829785324
##########
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:
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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[GitHub] [flink] dianfu commented on a change in pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] javacaoyu commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1072148169
@dianfu Thanks for you guidance. I will fix these issues that you raised.
By the way, I'm interested about implement other methods like min/min_by/max/max_by etc.
When finish this PR, I can try to implement one of the methods first if you assign the jira task to me.
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[GitHub] [flink] flinkbot commented on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #19126:
URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/19126#issuecomment-1070385440
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[GitHub] [flink] flinkbot edited a comment on pull request #19126: [FLINK-26609][python] Support sum operation in KeyedStream
Posted by GitBox <gi...@apache.org>.
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