hequn8128 commented on a change in pull request #9766: [FLINK-14018][python] Add Python building blocks to make sure the basic functionality of Python ScalarFunction could work URL: https://github.com/apache/flink/pull/9766#discussion_r328685073
########## File path: flink-python/pyflink/fn_execution/operations.py ########## @@ -0,0 +1,261 @@ +################################################################################ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +################################################################################ + +from abc import abstractmethod, ABCMeta + +from apache_beam.runners.worker import operation_specs +from apache_beam.runners.worker import bundle_processor +from apache_beam.runners.worker.operations import Operation + +from pyflink.fn_execution import flink_fn_execution_pb2 + +SCALAR_FUNCTION_URN = "flink:transform:scalar_function:v1" + + +class InputGetter(object): + """ + Base class for get an input argument for a :class:`UserDefinedFunction`. + """ + __metaclass__ = ABCMeta + + def open(self): + pass + + def close(self): + pass + + @abstractmethod + def get(self, value): + pass + + +class OffsetInputGetter(InputGetter): + """ + InputGetter for the input argument which is a column of the input row. + + :param input_offset: the offset of the column in the input row + """ + + def __init__(self, input_offset): + self.input_offset = input_offset + + def get(self, value): + return value[self.input_offset] + + +class ScalarFunctionInputGetter(InputGetter): + """ + InputGetter for the input argument which is a Python :class:`ScalarFunction`. This is used for + chaining Python functions. + + :param scalar_function_proto: the proto representation of the Python :class:`ScalarFunction` + """ + + def __init__(self, scalar_function_proto): + self.scalar_function_invoker = create_scalar_function_invoker(scalar_function_proto) + + def open(self): + self.scalar_function_invoker.invoke_open() + + def close(self): + self.scalar_function_invoker.invoke_close() + + def get(self, value): + return self.scalar_function_invoker.invoke_eval(value) + + +class ScalarFunctionInvoker(object): + """ + An abstraction that can be used to execute :class:`ScalarFunction` methods. + + A ScalarFunctionInvoker describes a particular way for invoking methods of a + :class:`ScalarFunction`. + + :param scalar_function: the :class:`ScalarFunction` to execute + :param inputs: the input arguments for the :class:`ScalarFunction` + """ + + def __init__(self, scalar_function, inputs): + self.scalar_function = scalar_function + self.input_getters = [] + for input in inputs: + if input.HasField("udf"): + # for chaining Python UDF input: the input argument is a Python ScalarFunction + self.input_getters.append(ScalarFunctionInputGetter(input.udf)) + else: + # the input argument is a column of the input row + self.input_getters.append(OffsetInputGetter(input.inputOffset)) + + def invoke_open(self): + """ + Invokes the ScalarFunction.open() function. + """ + for input_getter in self.input_getters: + input_getter.open() + # set the FunctionContext to None for now + self.scalar_function.open(None) + + def invoke_close(self): + """ + Invokes the ScalarFunction.close() function. + """ + for input_getter in self.input_getters: + input_getter.close() + self.scalar_function.close() + + def invoke_eval(self, value): + """ + Invokes the ScalarFunction.eval() function. + + :param value: the input element for which eval() method should be invoked + """ + args = [input_getter.get(value) for input_getter in self.input_getters] + return self.scalar_function.eval(*args) + + +def create_scalar_function_invoker(scalar_function_proto): + """ + Creates :class:`ScalarFunctionInvoker` from the proto representation of a + :class:`ScalarFunction`. + + :param scalar_function_proto: the proto representation of the Python :class:`ScalarFunction` + :return: :class:`ScalarFunctionInvoker`. + """ + import cloudpickle + scalar_function = cloudpickle.loads(scalar_function_proto.payload) + return ScalarFunctionInvoker(scalar_function, scalar_function_proto.inputs) + + +class ScalarFunctionRunner(object): + """ + The runner which is responsible for executing the scalar functions and send the + execution results back to the remote Java operator. + + :param udfs_proto: protocol representation for the scalar functions to execute + :param main_receivers: Receiver objects which is responsible for sending the execution + results back the the remote Java operator + """ + + def __init__(self, udfs_proto, main_receivers): + self.scalar_function_invokers = [create_scalar_function_invoker(f) for f in + udfs_proto] + from apache_beam.runners.common import _OutputProcessor + self.output_processor = _OutputProcessor( + window_fn=None, + main_receivers=main_receivers, + tagged_receivers=None, + per_element_output_counter=None) + + def open(self): + for invoker in self.scalar_function_invokers: + invoker.invoke_open() + + def close(self): + for invoker in self.scalar_function_invokers: + invoker.invoke_close() + + def start(self): + pass + + def finish(self): + pass + + def process(self, windowed_value): + results = [invoker.invoke_eval(windowed_value.value) for invoker in + self.scalar_function_invokers] + from pyflink.table import Row + result = Row(*results) + # send the execution results back + self.output_processor.process_outputs(windowed_value, [result]) + + +class ScalarFunctionOperation(Operation): + """ + An operation that will execute ScalarFunctions for each input element. + """ + + def __init__(self, name, spec, counter_factory, sampler, consumers): + super(ScalarFunctionOperation, self).__init__(name, spec, counter_factory, sampler) + for tag, op_consumers in consumers.items(): + for consumer in op_consumers: + self.add_receiver(consumer, 0) + + def setup(self): + with self.scoped_start_state: + super(ScalarFunctionOperation, self).setup() + + self.scalar_function_runner = ScalarFunctionRunner( + self.spec.serialized_fn, + main_receivers=self.receivers[0]) + + self.scalar_function_runner.open() Review comment: setup() will be called at the beginning of each bundle while the open() method can only be called once. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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