def _test_flatten_input_visitor(self, input_type, output_type, num_inputs):
p = TestPipeline()
inputs = []
for _ in range(num_inputs):
input_pcoll = PCollection(p)
input_pcoll.element_type = input_type
inputs.append(input_pcoll)
output_pcoll = PCollection(p)
output_pcoll.element_type = output_type
flatten = AppliedPTransform(None, beam.Flatten(), "label", inputs)
flatten.add_output(output_pcoll, None)
DataflowRunner.flatten_input_visitor().visit_transform(flatten)
for _ in range(num_inputs):
self.assertEqual(inputs[0].element_type, output_type)
def _test_flatten_input_visitor(self, input_type, output_type, num_inputs):
p = TestPipeline()
inputs = []
for _ in range(num_inputs):
input_pcoll = PCollection(p)
input_pcoll.element_type = input_type
inputs.append(input_pcoll)
output_pcoll = PCollection(p)
output_pcoll.element_type = output_type
flatten = AppliedPTransform(None, beam.Flatten(), "label", inputs)
flatten.add_output(output_pcoll, None)
DataflowRunner.flatten_input_visitor().visit_transform(flatten)
for _ in range(num_inputs):
self.assertEqual(inputs[0].element_type, output_type)
def test_gbk_then_flatten_input_visitor(self):
p = TestPipeline(runner=DataflowRunner(),
options=PipelineOptions(self.default_properties))
none_str_pc = p | 'c1' >> beam.Create({None: 'a'})
none_int_pc = p | 'c2' >> beam.Create({None: 3})
flat = (none_str_pc, none_int_pc) | beam.Flatten()
_ = flat | beam.GroupByKey()
# This may change if type inference changes, but we assert it here
# to make sure the check below is not vacuous.
self.assertNotIsInstance(flat.element_type, typehints.TupleConstraint)
p.visit(DataflowRunner.group_by_key_input_visitor())
p.visit(DataflowRunner.flatten_input_visitor())
# The dataflow runner requires gbk input to be tuples *and* flatten
# inputs to be equal to their outputs. Assert both hold.
self.assertIsInstance(flat.element_type, typehints.TupleConstraint)
self.assertEqual(flat.element_type, none_str_pc.element_type)
self.assertEqual(flat.element_type, none_int_pc.element_type)
def test_gbk_then_flatten_input_visitor(self):
p = TestPipeline(
runner=DataflowRunner(),
options=PipelineOptions(self.default_properties))
none_str_pc = p | 'c1' >> beam.Create({None: 'a'})
none_int_pc = p | 'c2' >> beam.Create({None: 3})
flat = (none_str_pc, none_int_pc) | beam.Flatten()
_ = flat | beam.GroupByKey()
# This may change if type inference changes, but we assert it here
# to make sure the check below is not vacuous.
self.assertNotIsInstance(flat.element_type, typehints.TupleConstraint)
p.visit(DataflowRunner.group_by_key_input_visitor())
p.visit(DataflowRunner.flatten_input_visitor())
# The dataflow runner requires gbk input to be tuples *and* flatten
# inputs to be equal to their outputs. Assert both hold.
self.assertIsInstance(flat.element_type, typehints.TupleConstraint)
self.assertEqual(flat.element_type, none_str_pc.element_type)
self.assertEqual(flat.element_type, none_int_pc.element_type)
