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From Chamikara Jayalath <chamik...@google.com>
Subject Re: Create External Transform with WindowFn
Date Wed, 26 Aug 2020 01:28:47 GMT
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 <chamikara@google.com>
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 <boyuanz@google.com> 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 <robertwb@google.com>
>> 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 <boyuanz@google.com> 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!
>>>
>>

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