HuangXingBo commented on a change in pull request #13421:
URL: https://github.com/apache/flink/pull/13421#discussion_r491994679
##########
File path: flink-python/pyflink/fn_execution/beam/beam_operations_fast.pyx
##########
@@ -173,6 +173,28 @@ cdef class
DataStreamStatelessFunctionOperation(BeamStatelessFunctionOperation):
func = operation_utils.extract_data_stream_stateless_funcs(udfs)
return func, []
+
+cdef class PandasAggregateFunctionOperation(BeamStatelessFunctionOperation):
+ def __init__(self, name, spec, counter_factory, sampler, consumers):
+ super(PandasAggregateFunctionOperation, self).__init__(name, spec,
counter_factory,
+ sampler,
consumers)
+
+ def generate_func(self, udfs):
+ pandas_functions, variable_dict, user_defined_funcs = reduce(
+ lambda x, y: (
+ ','.join([x[0], y[0]]),
+ dict(chain(x[1].items(), y[1].items())),
+ x[2] + y[2]),
+ [operation_utils.extract_user_defined_function(udf) for udf in
udfs])
+ variable_dict['wrap_pandas_result'] =
operation_utils.wrap_pandas_result
+ mapper = eval('lambda value: wrap_pandas_result([%s])' %
pandas_functions, variable_dict)
+ if self._is_python_coder:
Review comment:
We currently only provide the implementation of arrow coder with python
version, so it is always true
##########
File path: flink-python/pyflink/table/tests/test_pandas_udaf.py
##########
@@ -0,0 +1,48 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from pyflink.table.types import DataTypes
+
+from pyflink.table.udf import udaf
+from pyflink.testing import source_sink_utils
+from pyflink.testing.test_case_utils import PyFlinkBlinkBatchTableTestCase
+
+
+class BatchPandasUDAFITTests(PyFlinkBlinkBatchTableTestCase):
+ def test_group_aggregate_function(self):
+ t = self.t_env.from_elements(
+ [(1, 2, 3), (3, 2, 3), (2, 1, 3), (1, 5, 4), (1, 8, 6), (2, 3, 4)],
+ DataTypes.ROW(
+ [DataTypes.FIELD("a", DataTypes.TINYINT()),
+ DataTypes.FIELD("b", DataTypes.SMALLINT()),
+ DataTypes.FIELD("c", DataTypes.INT())]))
+
+ table_sink = source_sink_utils.TestAppendSink(
+ ['a', 'b'],
+ [DataTypes.TINYINT(), DataTypes.FLOAT()])
+ self.t_env.register_table_sink("Results", table_sink)
+ t.group_by("a") \
+ .select(t.a, mean_udaf(t.b)) \
+ .execute_insert("Results") \
+ .wait()
+ actual = source_sink_utils.results()
+ self.assert_equals(actual, ["1,5.0", "2,2.0", "3,2.0"])
+
+
+@udaf(result_type=DataTypes.FLOAT(), udaf_type="pandas")
Review comment:
Yes.Make sense.
##########
File path: flink-python/pyflink/table/udf.py
##########
@@ -313,6 +435,70 @@ def _create_judtf(self):
return j_table_function
+class UserDefinedAggregateFunctionWrapper(UserDefinedFunctionWrapper):
Review comment:
Yes. Make sense
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