pyflink.datastream.data_stream.KeyedStream.sum#

KeyedStream.sum(position_to_sum: Union[int, str] = 0) → pyflink.datastream.data_stream.DataStream[source]#

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")

Parameters: position_to_sum – The field position in the data points to sum, type can be int which indicates the index of the column to operate on or str which indicates the name of the column to operate on.
Returns: The transformed DataStream.

New in version 1.16.0.