Support custom coders in Reshuffle

CHANGES.md

@@ -66,9 +66,12 @@
 
 ## New Features / Improvements
 
+* Support custom coders in Reshuffle ([#29908](https://github.com/apache/beam/issues/29908), [#33356](https://github.com/apache/beam/issues/33356)).
+
 * X feature added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Breaking Changes
+* [Python] Reshuffle is now more faithful to user-specified typehints and coders. Previously, Reshuffle could incorrectly use FastPrimitivesCoder in some pipelines. This update corrects that behavior. However, it may be a breaking change for pipelines with incorrect typehints for Reshuffle. If you encounter issues after upgrading to this Beam version, you can temporarily specify update_compatibility_version to an older Beam version (e.g. 2.63.0) in your pipeline options as a workaround. The recommended long-term solution is to correct the inaccurate typehints in your pipeline. ([#33932](https://github.com/apache/beam/pull/33932))
 
 * X behavior was changed ([#X](https://github.com/apache/beam/issues/X)).
 

sdks/python/apache_beam/coders/coders.py

@@ -1445,6 +1445,17 @@ def __hash__(self):
     return hash(
         (self.wrapped_value_coder, self.timestamp_coder, self.window_coder))
 
+  @classmethod
+  def from_type_hint(cls, typehint, registry):
+    # type: (Any, CoderRegistry) -> WindowedValueCoder
+    # Ideally this'd take two parameters so that one could hint at
+    # the window type as well instead of falling back to the
+    # pickle coders.
+    return cls(registry.get_coder(typehint.inner_type))
+
+  def to_type_hint(self):
+    return typehints.WindowedValue[self.wrapped_value_coder.to_type_hint()]
+
 
 Coder.register_structured_urn(
     common_urns.coders.WINDOWED_VALUE.urn, WindowedValueCoder)

sdks/python/apache_beam/coders/coders_test.py

@@ -276,6 +276,12 @@ def test_numpy_int(self):
         _ = indata | "CombinePerKey" >> beam.CombinePerKey(sum)
 
 
+class WindowedValueCoderTest(unittest.TestCase):
+  def test_to_type_hint(self):
+    coder = coders.WindowedValueCoder(coders.VarIntCoder())
+    self.assertEqual(coder.to_type_hint(), typehints.WindowedValue[int])  # type: ignore[misc]
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()

sdks/python/apache_beam/coders/typecoders.py

@@ -94,6 +94,8 @@ def register_standard_coders(self, fallback_coder):
     self._register_coder_internal(str, coders.StrUtf8Coder)
     self._register_coder_internal(typehints.TupleConstraint, coders.TupleCoder)
     self._register_coder_internal(typehints.DictConstraint, coders.MapCoder)
+    self._register_coder_internal(
+        typehints.WindowedTypeConstraint, coders.WindowedValueCoder)
     # Default fallback coders applied in that order until the first matching
     # coder found.
     default_fallback_coders = [

sdks/python/apache_beam/transforms/util.py

@@ -32,6 +32,7 @@
 from collections.abc import Iterable
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import Optional
 from typing import TypeVar
 from typing import Union
 
@@ -40,6 +41,7 @@
 from apache_beam import pvalue
 from apache_beam import typehints
 from apache_beam.metrics import Metrics
+from apache_beam.options import pipeline_options
 from apache_beam.portability import common_urns
 from apache_beam.portability.api import beam_runner_api_pb2
 from apache_beam.pvalue import AsSideInput
@@ -71,11 +73,13 @@
 from apache_beam.transforms.window import TimestampedValue
 from apache_beam.typehints import trivial_inference
 from apache_beam.typehints.decorators import get_signature
+from apache_beam.typehints.native_type_compatibility import TypedWindowedValue
 from apache_beam.typehints.sharded_key_type import ShardedKeyType
 from apache_beam.utils import shared
 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
@@ -102,6 +106,8 @@
 V = TypeVar('V')
 T = TypeVar('T')
 
+RESHUFFLE_TYPEHINT_BREAKING_CHANGE_VERSION = "2.64.0"
+
 
 class CoGroupByKey(PTransform):
   """Groups results across several PCollections by key.
@@ -922,6 +928,25 @@ def get_window_coder(self):
     return self._window_coder
 
 
+def is_compat_version_prior_to_breaking_change(
+    update_compatibility_version, breaking_change_version):
+  # This function is used in a branch statement to determine whether we should
+  # keep the old behavior prior to a breaking change or use the new behavior.
+  # - If update_compatibility_version < breaking_change_version, we will return
+  #   True and keep the old behavior.
+  # - If update_compatibility_version is None or >= breaking_change_version, we
+  #   will return False and use the behavior from the breaking change.
+  if update_compatibility_version is None:
+    return False
+
+  compat_version = tuple(map(int, update_compatibility_version.split('.')[0:3]))
+  change_version = tuple(map(int, breaking_change_version.split('.')[0:3]))
+  for i in range(min(len(compat_version), len(change_version))):
+    if compat_version[i] < change_version[i]:
+      return True
+  return False
+
+
 @typehints.with_input_types(tuple[K, V])
 @typehints.with_output_types(tuple[K, V])
 class ReshufflePerKey(PTransform):
@@ -931,6 +956,8 @@ class ReshufflePerKey(PTransform):
   transforms.
   """
   def expand(self, pcoll):
+    compat_version = pcoll.pipeline.options.view_as(
+        pipeline_options.StreamingOptions).update_compatibility_version
     windowing_saved = pcoll.windowing
     if windowing_saved.is_default():
       # In this (common) case we can use a trivial trigger driver
@@ -951,6 +978,14 @@ def restore_timestamps(element):
             window.GlobalWindows.windowed_value((key, value), timestamp)
             for (value, timestamp) in values
         ]
+
+      if is_compat_version_prior_to_breaking_change(
+          compat_version, RESHUFFLE_TYPEHINT_BREAKING_CHANGE_VERSION):
+        ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any)
+      else:
+        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
@@ -964,7 +999,12 @@ 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)
+      if is_compat_version_prior_to_breaking_change(
+          compat_version, RESHUFFLE_TYPEHINT_BREAKING_CHANGE_VERSION):
+        ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any)
+      else:
+        ungrouped = pcoll | Map(reify_timestamps).with_input_types(
+            tuple[K, V]).with_output_types(tuple[K, TypedWindowedValue[V]])
 
     # 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
@@ -1012,11 +1052,19 @@ def __init__(self, num_buckets=None):
 
   def expand(self, pcoll):
     # type: (pvalue.PValue) -> pvalue.PCollection
+    compat_version = pcoll.pipeline.options.view_as(
+        pipeline_options.StreamingOptions).update_compatibility_version
+    if is_compat_version_prior_to_breaking_change(
+        compat_version, RESHUFFLE_TYPEHINT_BREAKING_CHANGE_VERSION):
+      reshuffle_step = ReshufflePerKey()
+    else:
+      reshuffle_step = ReshufflePerKey().with_input_types(
+          tuple[int, T]).with_output_types(tuple[int, T])
     return (
         pcoll | 'AddRandomKeys' >>
         Map(lambda t: (random.randrange(0, self.num_buckets), t)
             ).with_input_types(T).with_output_types(tuple[int, T])
-        | ReshufflePerKey()
+        | reshuffle_step
         | '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 +1010,82 @@ def format_with_timestamp(element, timestamp=beam.DoFn.TimestampParam):
           equal_to(expected_data),
           label="formatted_after_reshuffle")
 
+  global _Unpicklable
+  global _UnpicklableCoder
+
+  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
+
+  def reshuffle_unpicklable_in_global_window_helper(
+      self, update_compatibility_version=None):
+    with TestPipeline(options=PipelineOptions(
+        update_compatibility_version=update_compatibility_version)) 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))
+
+  def test_reshuffle_unpicklable_in_global_window(self):
+    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
+
+    self.reshuffle_unpicklable_in_global_window_helper()
+    # An exception is raised when running reshuffle on unpicklable objects
+    # prior to 2.64.0
+    self.assertRaises(
+        RuntimeError,
+        self.reshuffle_unpicklable_in_global_window_helper,
+        "2.63.0")
+
+  def reshuffle_unpicklable_in_non_global_window_helper(
+      self, update_compatibility_version=None):
+    with TestPipeline(options=PipelineOptions(
+        update_compatibility_version=update_compatibility_version)) as pipeline:
+      data = [_Unpicklable(i) for i in range(5)]
+      expected_data = [0, 0, 0, 10, 10, 10, 20, 20, 20, 30, 30, 30, 40, 40, 40]
+      result = (
+          pipeline
+          | beam.Create(data)
+          | beam.WindowInto(window.SlidingWindows(size=3, period=1))
+          | beam.Reshuffle()
+          | beam.Map(lambda u: u.value * 10))
+      assert_that(result, equal_to(expected_data))
+
+  def test_reshuffle_unpicklable_in_non_global_window(self):
+    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
+
+    self.reshuffle_unpicklable_in_non_global_window_helper()
+    # An exception is raised when running reshuffle on unpicklable objects
+    # prior to 2.64.0
+    self.assertRaises(
+        RuntimeError,
+        self.reshuffle_unpicklable_in_non_global_window_helper,
+        "2.63.0")
+
 
 class WithKeysTest(unittest.TestCase):
   def setUp(self):

sdks/python/apache_beam/typehints/native_type_compatibility.py

@@ -25,9 +25,13 @@
 import sys
 import types
 import typing
+from typing import Generic
+from typing import TypeVar
 
 from apache_beam.typehints import typehints
 
+T = TypeVar('T')
+
 _LOGGER = logging.getLogger(__name__)
 
 # Describes an entry in the type map in convert_to_beam_type.
@@ -277,6 +281,18 @@ def is_builtin(typ):
   return getattr(typ, '__origin__', None) in _BUILTINS
 
 
+# During type inference of WindowedValue, we need to pass in the inner value
+# type. This cannot be achieved immediately with WindowedValue class because it
+# is not parameterized. Changing it to a generic class (e.g. WindowedValue[T])
+# could work in theory. However, the class is cythonized and it seems that
+# cython does not handle generic classes well.
+# The workaround here is to create a separate class solely for the type
+# inference purpose. This class should never be used for creating instances.
+class TypedWindowedValue(Generic[T]):
+  def __init__(self, *args, **kwargs):
+    raise NotImplementedError("This class is solely for type inference")
+
+
 def convert_to_beam_type(typ):
   """Convert a given typing type to a Beam type.
 
@@ -385,6 +401,10 @@ def convert_to_beam_type(typ):
           match=_match_is_exactly_collection,
           arity=1,
           beam_type=typehints.Collection),
+      _TypeMapEntry(
+          match=_match_issubclass(TypedWindowedValue),
+          arity=1,
+          beam_type=typehints.WindowedValue),
   ]
 
   # Find the first matching entry.

sdks/python/apache_beam/typehints/typehints.py

@@ -1213,6 +1213,15 @@ def type_check(self, instance):
               repr(self.inner_type),
               instance.value.__class__.__name__))
 
+  def bind_type_variables(self, bindings):
+    bound_inner_type = bind_type_variables(self.inner_type, bindings)
+    if bound_inner_type == self.inner_type:
+      return self
+    return WindowedValue[bound_inner_type]
+
+  def __repr__(self):
+    return 'WindowedValue[%s]' % repr(self.inner_type)
+
 
 class GeneratorHint(IteratorHint):
   """A Generator type hint.