Add unit test for named tuple and dataclasses encoded by RowCoder and passing through GBK

Author: Abacn

sdks/python/apache_beam/typehints/row_type_test.py

@@ -0,0 +1,86 @@
+#
+# 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.
+#
+
+"""Unit tests for the Beam Row typing functionality."""
+
+from dataclasses import dataclass
+import typing
+import unittest
+
+import apache_beam as beam
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import assert_that
+from apache_beam.testing.util import equal_to
+from apache_beam.typehints import row_type
+
+
+class RowTypeTest(unittest.TestCase):
+  @staticmethod
+  def _check_key_type_and_count(x) -> int:
+    key_type = type(x[0])
+    if not row_type._user_type_is_generated(key_type):
+      raise RuntimeError("Expect type after GBK to be generated user type")
+
+    return len(x[1])
+
+  def test_group_by_key_namedtuple(self):
+    MyNamedTuple = typing.NamedTuple(
+        "MyNamedTuple", [("id", int), ("name", str)])
+
+    beam.coders.typecoders.registry.register_coder(
+        MyNamedTuple, beam.coders.RowCoder)
+
+    def generate(num: int):
+      for i in range(100):
+        yield (MyNamedTuple(i, 'a'), num)
+
+    pipeline = TestPipeline(is_integration_test=False)
+
+    with pipeline as p:
+      result = (
+          p
+          | 'Create' >> beam.Create([i for i in range(10)])
+          | 'Generate' >> beam.ParDo(generate).with_output_types(
+              tuple[MyNamedTuple, int])
+          | 'GBK' >> beam.GroupByKey()
+          | 'Count Elements' >> beam.Map(self._check_key_type_and_count))
+      assert_that(result, equal_to([10] * 100))
+
+  def test_group_by_key_dataclass(self):
+    @dataclass
+    class MyDataClass:
+      id: int
+      name: str
+
+    beam.coders.typecoders.registry.register_coder(
+        MyDataClass, beam.coders.RowCoder)
+
+    def generate(num: int):
+      for i in range(100):
+        yield (MyDataClass(i, 'a'), num)
+
+    pipeline = TestPipeline(is_integration_test=False)
+
+    with pipeline as p:
+      result = (
+          p
+          | 'Create' >> beam.Create([i for i in range(10)])
+          | 'Generate' >> beam.ParDo(generate).with_output_types(
+              tuple[MyDataClass, int])
+          | 'GBK' >> beam.GroupByKey()
+          | 'Count Elements' >> beam.Map(self._check_key_type_and_count))
+      assert_that(result, equal_to([10] * 100))