Hi Dian,
I follow up with this PR https://github.com/apache/flink/pull/15790
On Tue, Apr 27, 2021 at 11:03 PM Dian Fu <dian0511...@gmail.com> wrote:
> Hi Yik San,
>
> Make sense to me. :)
>
> Regards,
> Dian
>
> 2021年4月27日 下午9:50，Yik San Chan <evan.chanyik...@gmail.com> 写道：
>
> 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
>>>
>>>
>>>
>>
>
