Also it's strange that Java used (beam:window_fn:serialized_java:v1) for the URN here instead of "beam:window_fn:fixed_windows:v1" [1] which is what is being registered by Python [2]. This seems to be the immediate issue. Tracking bug for supporting custom windows is https://issues.apache.org/jira/browse/BEAM-10507.
[1] https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/standard_window_fns.proto#L55 [2] https://github.com/apache/beam/blob/bd4df94ae10a7e7b0763c1917746d2faf5aeed6c/sdks/python/apache_beam/transforms/window.py#L449 On Tue, Aug 25, 2020 at 6:07 PM Chamikara Jayalath <[email protected]> wrote: > Pipelines that use external WindowingStrategies might be failing during > proto -> object -> proto conversion we do today. This limitation will go > away once Dataflow directly starts reading Beam protos. We are working on > this now. > > Thanks, > Cham > > On Tue, Aug 25, 2020 at 5:38 PM Boyuan Zhang <[email protected]> wrote: > >> Thanks, Robert! I want to add more details on my External PTransform: >> >> MyExternalPTransform -- expand to -- ParDo() -> WindowInto(FixWindow) >> -> ParDo() -> output void >> | >> -> >> ParDo() -> output PCollection to Python SDK >> The full stacktrace: >> >> INFO:root:Using Java SDK harness container image >> dataflow-dev.gcr.io/boyuanz/java:latest >> Starting expansion service at localhost:53569 >> Aug 13, 2020 7:42:11 PM >> org.apache.beam.sdk.expansion.service.ExpansionService >> loadRegisteredTransforms >> INFO: Registering external transforms: [beam:external:java:kafka:read:v1, >> beam:external:java:kafka:write:v1, beam:external:java:jdbc:read_rows:v1, >> beam:external:java:jdbc:write:v1, beam:external:java:generate_sequence:v1] >> beam:external:java:kafka:read:v1: >> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader$$Lambda$8/0x0000000800b2a440@4ac68d3e >> beam:external:java:kafka:write:v1: >> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader$$Lambda$8/0x0000000800b2a440@277c0f21 >> beam:external:java:jdbc:read_rows:v1: >> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader$$Lambda$8/0x0000000800b2a440@6073f712 >> beam:external:java:jdbc:write:v1: >> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader$$Lambda$8/0x0000000800b2a440@43556938 >> beam:external:java:generate_sequence:v1: >> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader$$Lambda$8/0x0000000800b2a440@3d04a311 >> WARNING:apache_beam.options.pipeline_options_validator:Option --zone is >> deprecated. Please use --worker_zone instead. >> Aug 13, 2020 7:42:12 PM >> org.apache.beam.sdk.expansion.service.ExpansionService expand >> INFO: Expanding 'WriteToKafka' with URN 'beam:external:java:kafka:write:v1' >> Aug 13, 2020 7:42:14 PM >> org.apache.beam.sdk.expansion.service.ExpansionService expand >> INFO: Expanding 'ReadFromKafka' with URN 'beam:external:java:kafka:read:v1' >> >> WARNING:root:Make sure that locally built Python SDK docker image has Python >> 3.6 interpreter. >> INFO:root:Using Python SDK docker image: >> apache/beam_python3.6_sdk:2.24.0.dev. If the image is not available at >> local, we will try to pull from hub.docker.com >> Traceback (most recent call last): >> File "<embedded module '_launcher'>", line 165, in run_filename_as_main >> File "<embedded module '_launcher'>", line 39, in _run_code_in_main >> File "apache_beam/integration/cross_language_kafkaio_test.py", line 87, in >> <module> >> run() >> File "apache_beam/integration/cross_language_kafkaio_test.py", line 82, in >> run >> test_method(beam.Pipeline(options=pipeline_options)) >> File "apache_beam/io/external/xlang_kafkaio_it_test.py", line 94, in >> run_xlang_kafkaio >> pipeline.run(False) >> File "apache_beam/pipeline.py", line 534, in run >> return self.runner.run_pipeline(self, self._options) >> File "apache_beam/runners/dataflow/dataflow_runner.py", line 496, in >> run_pipeline >> allow_proto_holders=True) >> File "apache_beam/pipeline.py", line 879, in from_runner_api >> p.transforms_stack = [context.transforms.get_by_id(root_transform_id)] >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1266, in from_runner_api >> part = context.transforms.get_by_id(transform_id) >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pipeline.py", line 1272, in from_runner_api >> id in proto.outputs.items() >> File "apache_beam/pipeline.py", line 1272, in <dictcomp> >> id in proto.outputs.items() >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/pvalue.py", line 217, in from_runner_api >> proto.windowing_strategy_id), >> File "apache_beam/runners/pipeline_context.py", line 95, in get_by_id >> self._id_to_proto[id], self._pipeline_context) >> File "apache_beam/transforms/core.py", line 2597, in from_runner_api >> windowfn=WindowFn.from_runner_api(proto.window_fn, context), >> File "apache_beam/utils/urns.py", line 186, in from_runner_api >> parameter_type, constructor = cls._known_urns[fn_proto.urn] >> KeyError: 'beam:window_fn:serialized_java:v1' >> >> >> On Tue, Aug 25, 2020 at 5:12 PM Robert Bradshaw <[email protected]> >> wrote: >> >>> You should be able to use a WindowInto with any of the common >>> windowing operations (e.g. global, fixed, sliding, sessions) in an >>> external transform. You should also be able to window into an >>> arbitrary WindowFn as long as it produces standards window types, but >>> if there's a bug here you could possibly work around it by windowing >>> into a more standard windowing fn before returning. >>> >>> What is the full traceback? >>> >>> On Tue, Aug 25, 2020 at 5:02 PM Boyuan Zhang <[email protected]> wrote: >>> > >>> > Hi team, >>> > >>> > I'm trying to create an External transform in Java SDK, which expands >>> into several ParDo and a Window.into(FixWindow). When I use this transform >>> in Python SDK, I get an pipeline construction error: >>> > >>> > apache_beam/utils/urns.py", line 186, in from_runner_api >>> > parameter_type, constructor = cls._known_urns[fn_proto.urn] >>> > KeyError: 'beam:window_fn:serialized_java:v1' >>> > >>> > Is it expected that I cannot use a Window.into when building External >>> Ptransform? Or do I miss anything here? >>> > >>> > >>> > Thanks for your help! >>> >>
