Source code for apache_beam.transforms.combinefn_lifecycle_pipeline

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# pytype: skip-file

from typing import Set
from typing import Tuple

import apache_beam as beam
from apache_beam.options.pipeline_options import TypeOptions
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to
from apache_beam.transforms import combiners
from apache_beam.transforms import trigger
from apache_beam.transforms import userstate
from apache_beam.transforms import window
from apache_beam.typehints import with_input_types
from apache_beam.typehints import with_output_types


[docs]@with_input_types(int) @with_output_types(int) class CallSequenceEnforcingCombineFn(beam.CombineFn): instances = set() # type: Set[CallSequenceEnforcingCombineFn] def __init__(self): super(CallSequenceEnforcingCombineFn, self).__init__() self._setup_called = False self._teardown_called = False
[docs] def setup(self, *args, **kwargs): assert not self._setup_called, 'setup should not be called twice' assert not self._teardown_called, 'setup should be called before teardown' # Keep track of instances so that we can check if teardown is called # properly after pipeline execution. self.instances.add(self) self._setup_called = True
[docs] def create_accumulator(self, *args, **kwargs): assert self._setup_called, 'setup should have been called' assert not self._teardown_called, 'teardown should not have been called' return 0
[docs] def add_input(self, mutable_accumulator, element, *args, **kwargs): assert self._setup_called, 'setup should have been called' assert not self._teardown_called, 'teardown should not have been called' mutable_accumulator += element return mutable_accumulator
[docs] def add_inputs(self, mutable_accumulator, elements, *args, **kwargs): return self.add_input(mutable_accumulator, sum(elements))
[docs] def merge_accumulators(self, accumulators, *args, **kwargs): assert self._setup_called, 'setup should have been called' assert not self._teardown_called, 'teardown should not have been called' return sum(accumulators)
[docs] def extract_output(self, accumulator, *args, **kwargs): assert self._setup_called, 'setup should have been called' assert not self._teardown_called, 'teardown should not have been called' return accumulator
[docs] def teardown(self, *args, **kwargs): assert self._setup_called, 'setup should have been called' assert not self._teardown_called, 'teardown should not be called twice' self._teardown_called = True
[docs]@with_input_types(Tuple[None, str]) @with_output_types(Tuple[int, str]) class IndexAssigningDoFn(beam.DoFn): state_param = beam.DoFn.StateParam( userstate.CombiningValueStateSpec( 'index', beam.coders.VarIntCoder(), CallSequenceEnforcingCombineFn()))
[docs] def process(self, element, state=state_param): _, value = element current_index = state.read() yield current_index, value state.add(1)
[docs]def run_combine(pipeline, input_elements=5, lift_combiners=True): # Calculate the expected result, which is the sum of an arithmetic sequence. # By default, this is equal to: 0 + 1 + 2 + 3 + 4 = 10 expected_result = input_elements * (input_elements - 1) / 2 # Enable runtime type checking in order to cover TypeCheckCombineFn by # the test. pipeline.get_pipeline_options().view_as(TypeOptions).runtime_type_check = True pipeline.get_pipeline_options().view_as( TypeOptions).allow_unsafe_triggers = True with pipeline as p: pcoll = p | 'Start' >> beam.Create(range(input_elements)) # Certain triggers, such as AfterCount, are incompatible with combiner # lifting. We can use that fact to prevent combiners from being lifted. if not lift_combiners: pcoll |= beam.WindowInto( window.GlobalWindows(), trigger=trigger.AfterCount(input_elements), accumulation_mode=trigger.AccumulationMode.DISCARDING) # Pass an additional 'None' in order to cover _CurriedFn by the test. pcoll |= 'Do' >> beam.CombineGlobally( combiners.SingleInputTupleCombineFn( CallSequenceEnforcingCombineFn(), CallSequenceEnforcingCombineFn()), None).with_fanout(fanout=1) assert_that(pcoll, equal_to([(expected_result, expected_result)]))
[docs]def run_pardo(pipeline, input_elements=10): with pipeline as p: _ = ( p | 'Start' >> beam.Create(('Hello' for _ in range(input_elements))) | 'KeyWithNone' >> beam.Map(lambda elem: (None, elem)) | 'Do' >> beam.ParDo(IndexAssigningDoFn()))