Hi Dian,
Wow, this is unexpected 😮 How about adding documentations to Python UDF
about this? Again it can be time consuming to figure this out. Maybe:
To be able to run Python UDFs in any non-local mode, it is recommended to
include your UDF definitions using -pyfs config option, if your UDFs live
outside of the file where the main() function is defined.
What do you think?
Best,
Yik San
On Tue, Apr 27, 2021 at 9:24 PM Dian Fu <dian0511...@gmail.com> wrote:
> I guess this is the magic of cloud pickle. PyFlink depends on cloud pickle
> to serialize the Python UDF.
>
> For the latter case, I guess the whole Python UDF implementation will be
> serialized. However, for the previous case, only the path of the class is
> serialized.
>
> Regards,
> Dian
>
> 2021年4月27日 下午8:52，Yik San Chan <evan.chanyik...@gmail.com> 写道：
>
> Hi Dian,
>
> Thanks! Adding -pyfs definitely helps.
>
> However, I am curious. If I define my udf this way:
>
> ```python
> @udf(input_types=[DataTypes.STRING()], result_type=DataTypes.STRING())
> def decrypt(s):
> import pandas as pd
> d = pd.read_csv('resources.zip/resources/crypt.csv', header=None,
> index_col=0, squeeze=True).to_dict()
> return d.get(s, "unknown")
> ```
>
> I can `flink run` without having to specify -pyfs option. The code can
> also be found in the commit
> https://github.com/YikSanChan/pyflink-quickstart/commit/cd003ca7d36583999dbb5ffd45958762e4323607.
> I wonder why?
>
> Best,
> Yik San
>
> On Tue, Apr 27, 2021 at 8:13 PM Dian Fu <dian0511...@gmail.com> wrote:
>
>> Hi Yik San,
>>
>> From the exception message, it’s clear that it could not find module
>> `decrypt_fun` during execution.
>>
>> You need to specify file `decrypt_fun.py` as a dependency during
>> submitting the job, e.g. via -pyfs command line arguments. Otherwise, this
>> file will not be available during execution.
>>
>> Regards,
>> Dian
>>
>> 2021年4月27日 下午8:01，Yik San Chan <evan.chanyik...@gmail.com> 写道：
>>
>> Hi,
>>
>> Here's the reproducible code sample:
>> https://github.com/YikSanChan/pyflink-quickstart/tree/83526abca832f9ed5b8ce20be52fd506c45044d3
>>
>> I implement my Python UDF by extending the ScalarFunction base class in a
>> separate file named decrypt_fun.py, and try to import the udf into my main
>> python file named udf_use_resource.py.
>>
>> However, after I `flink run`, I find the error log in TaskManager log:
>>
>> ```
>> Caused by: java.lang.RuntimeException: Error received from SDK harness
>> for instruction 1: Traceback (most recent call last):
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 376, in get
>> processor = self.cached_bundle_processors[bundle_descriptor_id].pop()
>> IndexError: pop from empty list
>>
>> During handling of the above exception, another exception occurred:
>>
>> Traceback (most recent call last):
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 253, in _execute
>> response = task()
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 310, in <lambda>
>> lambda: self.create_worker().do_instruction(request), request)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 480, in do_instruction
>> getattr(request, request_type), request.instruction_id)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 509, in process_bundle
>> instruction_id, request.process_bundle_descriptor_id)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py",
>> line 382, in get
>> self.data_channel_factory)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 847, in __init__
>> self.ops = self.create_execution_tree(self.process_bundle_descriptor)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 902, in create_execution_tree
>> descriptor.transforms, key=topological_height, reverse=True)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 901, in <listcomp>
>> (transform_id, get_operation(transform_id)) for transform_id in sorted(
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 791, in wrapper
>> result = cache[args] = func(*args)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 885, in get_operation
>> pcoll_id in descriptor.transforms[transform_id].outputs.items()
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 885, in <dictcomp>
>> pcoll_id in descriptor.transforms[transform_id].outputs.items()
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 883, in <listcomp>
>> tag: [get_operation(op) for op in pcoll_consumers[pcoll_id]]
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 791, in wrapper
>> result = cache[args] = func(*args)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 888, in get_operation
>> transform_id, transform_consumers)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py",
>> line 1174, in create_operation
>> return creator(self, transform_id, transform_proto, payload, consumers)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/beam/beam_operations.py",
>> line 39, in create_scalar_function
>> operations.ScalarFunctionOperation)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/beam/beam_operations.py",
>> line 166, in _create_user_defined_function_operation
>> internal_operation_cls)
>> File "pyflink/fn_execution/beam/beam_operations_fast.pyx", line 110, in
>> pyflink.fn_execution.beam.beam_operations_fast.StatelessFunctionOperation.__init__
>> File "pyflink/fn_execution/beam/beam_operations_fast.pyx", line 49, in
>> pyflink.fn_execution.beam.beam_operations_fast.FunctionOperation.__init__
>> File "pyflink/fn_execution/beam/beam_operations_fast.pyx", line 114, in
>> pyflink.fn_execution.beam.beam_operations_fast.StatelessFunctionOperation.generate_operation
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/operations.py",
>> line 91, in __init__
>> super(ScalarFunctionOperation, self).__init__(spec)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/operations.py",
>> line 56, in __init__
>> self.func, self.user_defined_funcs =
>> self.generate_func(self.spec.serialized_fn)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/operations.py",
>> line 105, in generate_func
>> [operation_utils.extract_user_defined_function(udf) for udf in
>> serialized_fn.udfs])
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/operations.py",
>> line 105, in <listcomp>
>> [operation_utils.extract_user_defined_function(udf) for udf in
>> serialized_fn.udfs])
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/operation_utils.py",
>> line 86, in extract_user_defined_function
>> user_defined_func = pickle.loads(user_defined_function_proto.payload)
>> File
>> "/usr/local/anaconda3/envs/featflow-ml-env/lib/python3.7/site-packages/pyflink/fn_execution/pickle.py",
>> line 29, in loads
>> return cloudpickle.loads(payload)
>> ModuleNotFoundError: No module named 'decrypt_fun'
>>
>> at
>> org.apache.beam.runners.fnexecution.control.FnApiControlClient$ResponseStreamObserver.onNext(FnApiControlClient.java:177)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.runners.fnexecution.control.FnApiControlClient$ResponseStreamObserver.onNext(FnApiControlClient.java:157)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.stub.ServerCalls$StreamingServerCallHandler$StreamingServerCallListener.onMessage(ServerCalls.java:251)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.ForwardingServerCallListener.onMessage(ForwardingServerCallListener.java:33)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.Contexts$ContextualizedServerCallListener.onMessage(Contexts.java:76)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.internal.ServerCallImpl$ServerStreamListenerImpl.messagesAvailableInternal(ServerCallImpl.java:309)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.internal.ServerCallImpl$ServerStreamListenerImpl.messagesAvailable(ServerCallImpl.java:292)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.internal.ServerImpl$JumpToApplicationThreadServerStreamListener$1MessagesAvailable.runInContext(ServerImpl.java:782)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.internal.ContextRunnable.run(ContextRunnable.java:37)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> org.apache.beam.vendor.grpc.v1p26p0.io.grpc.internal.SerializingExecutor.run(SerializingExecutor.java:123)
>> ~[blob_p-c18fee26bdebc8cb6523e7161974631be9f3b3d0-8f27cc9e92a718bc9d3d138d1d2d49ca:1.12.0]
>> at
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
>> ~[?:1.8.0_282]
>> at
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
>> ~[?:1.8.0_282]
>> ... 1 more
>> ```
>>
>> I wonder why? If I move the Decrypt class into udf_use_resource.py,
>> everything works just fine.
>>
>> Thank you!
>>
>> Best,
>> Yik San
>>
>>
>>
>
