Fix custom coder not being used in Reshuffle (global window) (apache#…

…33339)

* Fix typehint in ReshufflePerKey on global window setting.

* Only update the type hint on global window setting. Need more work in non-global windows.

* Apply yapf

* Fix some failed tests.

* Revert change to setup.py

sdks/python/apache_beam/transforms/util.py

@@ -33,6 +33,7 @@
 from typing import Callable
 from typing import Iterable
 from typing import List
+from typing import Optional
 from typing import Tuple
 from typing import TypeVar
 from typing import Union
@@ -78,6 +79,7 @@
 from apache_beam.utils import windowed_value
 from apache_beam.utils.annotations import deprecated
 from apache_beam.utils.sharded_key import ShardedKey
+from apache_beam.utils.timestamp import Timestamp
 
 if TYPE_CHECKING:
   from apache_beam.runners.pipeline_context import PipelineContext
@@ -953,6 +955,10 @@ def restore_timestamps(element):
             window.GlobalWindows.windowed_value((key, value), timestamp)
             for (value, timestamp) in values
         ]
+
+      ungrouped = pcoll | Map(reify_timestamps).with_input_types(
+          Tuple[K, V]).with_output_types(
+              Tuple[K, Tuple[V, Optional[Timestamp]]])
     else:
 
       # typing: All conditional function variants must have identical signatures
@@ -966,7 +972,9 @@ def restore_timestamps(element):
         key, windowed_values = element
         return [wv.with_value((key, wv.value)) for wv in windowed_values]
 
-    ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any)
+      # TODO(https://github.com/apache/beam/issues/33356): Support reshuffling
+      # unpicklable objects with a non-global window setting.
+      ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any)
 
     # TODO(https://github.com/apache/beam/issues/19785) Using global window as
     # one of the standard window. This is to mitigate the Dataflow Java Runner
@@ -1018,7 +1026,8 @@ def expand(self, pcoll):
         pcoll | 'AddRandomKeys' >>
         Map(lambda t: (random.randrange(0, self.num_buckets), t)
             ).with_input_types(T).with_output_types(Tuple[int, T])
-        | ReshufflePerKey()
+        | ReshufflePerKey().with_input_types(Tuple[int, T]).with_output_types(
+            Tuple[int, T])
         | 'RemoveRandomKeys' >> Map(lambda t: t[1]).with_input_types(
             Tuple[int, T]).with_output_types(T))
 

sdks/python/apache_beam/transforms/util_test.py

@@ -1010,6 +1010,45 @@ def format_with_timestamp(element, timestamp=beam.DoFn.TimestampParam):
           equal_to(expected_data),
           label="formatted_after_reshuffle")
 
+  def test_reshuffle_unpicklable_in_global_window(self):
+    global _Unpicklable
+
+    class _Unpicklable(object):
+      def __init__(self, value):
+        self.value = value
+
+      def __getstate__(self):
+        raise NotImplementedError()
+
+      def __setstate__(self, state):
+        raise NotImplementedError()
+
+    class _UnpicklableCoder(beam.coders.Coder):
+      def encode(self, value):
+        return str(value.value).encode()
+
+      def decode(self, encoded):
+        return _Unpicklable(int(encoded.decode()))
+
+      def to_type_hint(self):
+        return _Unpicklable
+
+      def is_deterministic(self):
+        return True
+
+    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
+
+    with TestPipeline() as pipeline:
+      data = [_Unpicklable(i) for i in range(5)]
+      expected_data = [0, 10, 20, 30, 40]
+      result = (
+          pipeline
+          | beam.Create(data)
+          | beam.WindowInto(GlobalWindows())
+          | beam.Reshuffle()
+          | beam.Map(lambda u: u.value * 10))
+      assert_that(result, equal_to(expected_data))
+
 
 class WithKeysTest(unittest.TestCase):
   def setUp(self):