pyflink.datastream.data_stream.KeyedStream.min_by#

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

Applies an aggregation that gives the current element with the minimum value at the given position by the given key. An independent aggregate is kept per key. If more elements have the minimum value at the given position, the operator returns the first one by default.

Example(Tuple data):

>>> ds = env.from_collection([('a', 1), ('a', 2), ('b', 1), ('b', 5)])
>>> ds.key_by(lambda x: x[0]).min_by(1)

Example(Row data):

>>> 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]).min_by(1)

Example(Row data with fields name):

>>> 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]).min_by("value")

Parameters: position_to_min_by – The field position in the data points to minimize. The type can be int (field position) or str (field name). This is applicable to Tuple types, List types, Row types, and basic types (which is considered as having one field).
Returns: The transformed DataStream.

New in version 1.16.0.