def _create_pardo_operation(factory,
transform_id,
transform_proto,
consumers,
serialized_fn,
side_inputs_proto=None):
if side_inputs_proto:
input_tags_to_coders = factory.get_input_coders(transform_proto)
tagged_side_inputs = [
(tag,
beam.pvalue.SideInputData.from_runner_api(si, factory.context))
for tag, si in side_inputs_proto.items()
]
tagged_side_inputs.sort(key=lambda tag_si: int(
re.match('side([0-9]+)(-.*)?$', tag_si[0]).group(1)))
side_input_maps = [
StateBackedSideInputMap(factory.state_handler, transform_id, tag,
si, input_tags_to_coders[tag])
for tag, si in tagged_side_inputs
]
else:
side_input_maps = []
output_tags = list(transform_proto.outputs.keys())
# Hack to match out prefix injected by dataflow runner.
def mutate_tag(tag):
if 'None' in output_tags:
if tag == 'None':
return 'out'
else:
return 'out_' + tag
else:
return tag
dofn_data = pickler.loads(serialized_fn)
if not dofn_data[-1]:
# Windowing not set.
side_input_tags = side_inputs_proto or ()
pcoll_id, = [
pcoll for tag, pcoll in transform_proto.inputs.items()
if tag not in side_input_tags
]
windowing = factory.context.windowing_strategies.get_by_id(
factory.descriptor.pcollections[pcoll_id].windowing_strategy_id)
serialized_fn = pickler.dumps(dofn_data[:-1] + (windowing, ))
output_coders = factory.get_output_coders(transform_proto)
spec = operation_specs.WorkerDoFn(
serialized_fn=serialized_fn,
output_tags=[mutate_tag(tag) for tag in output_tags],
input=None,
side_inputs=None, # Fn API uses proto definitions and the Fn State API
output_coders=[output_coders[tag] for tag in output_tags])
return factory.augment_oldstyle_op(
operations.DoOperation(transform_proto.unique_name, spec,
factory.counter_factory, factory.state_sampler,
side_input_maps), transform_proto.unique_name,
consumers, output_tags)
def create(factory, transform_id, transform_proto, parameter, consumers):
dofn_data = pickler.loads(parameter.value)
if len(dofn_data) == 2:
# Has side input data.
serialized_fn, side_input_data = dofn_data
else:
# No side input data.
serialized_fn, side_input_data = parameter.value, []
def create_side_input(tag, coder):
# TODO(robertwb): Extract windows (and keys) out of element data.
# TODO(robertwb): Extract state key from ParDoPayload.
return operation_specs.WorkerSideInputSource(
tag=tag,
source=SideInputSource(factory.state_handler,
beam_fn_api_pb2.StateKey.MultimapSideInput(
key=side_input_tag(transform_id, tag)),
coder=coder))
output_tags = list(transform_proto.outputs.keys())
output_coders = factory.get_output_coders(transform_proto)
spec = operation_specs.WorkerDoFn(
serialized_fn=serialized_fn,
output_tags=output_tags,
input=None,
side_inputs=[
create_side_input(tag, coder) for tag, coder in side_input_data
],
output_coders=[output_coders[tag] for tag in output_tags])
return factory.augment_oldstyle_op(
operations.DoOperation(transform_proto.unique_name, spec,
factory.counter_factory, factory.state_sampler),
transform_proto.unique_name, consumers, output_tags)
def _create_pardo_operation(
factory, transform_id, transform_proto, consumers,
serialized_fn, side_input_data):
def create_side_input(tag, coder):
# TODO(robertwb): Extract windows (and keys) out of element data.
# TODO(robertwb): Extract state key from ParDoPayload.
return operation_specs.WorkerSideInputSource(
tag=tag,
source=SideInputSource(
factory.state_handler,
beam_fn_api_pb2.StateKey.MultimapSideInput(
key=side_input_tag(transform_id, tag)),
coder=coder))
output_tags = list(transform_proto.outputs.keys())
# Hack to match out prefix injected by dataflow runner.
def mutate_tag(tag):
if 'None' in output_tags:
if tag == 'None':
return 'out'
else:
return 'out_' + tag
else:
return tag
dofn_data = pickler.loads(serialized_fn)
if not dofn_data[-1]:
# Windowing not set.
pcoll_id, = transform_proto.inputs.values()
windowing = factory.context.windowing_strategies.get_by_id(
factory.descriptor.pcollections[pcoll_id].windowing_strategy_id)
serialized_fn = pickler.dumps(dofn_data[:-1] + (windowing,))
output_coders = factory.get_output_coders(transform_proto)
spec = operation_specs.WorkerDoFn(
serialized_fn=serialized_fn,
output_tags=[mutate_tag(tag) for tag in output_tags],
input=None,
side_inputs=[
create_side_input(tag, coder) for tag, coder in side_input_data],
output_coders=[output_coders[tag] for tag in output_tags])
return factory.augment_oldstyle_op(
operations.DoOperation(
transform_proto.unique_name,
spec,
factory.counter_factory,
factory.state_sampler),
transform_proto.unique_name,
consumers,
output_tags)
def create_execution_tree(self, descriptor):
# TODO(vikasrk): Add an id field to Coder proto and use that instead.
coders = {coder.function_spec.id: operation_specs.get_coder_from_spec(
json.loads(unpack_function_spec_data(coder.function_spec)))
for coder in descriptor.coders}
counter_factory = counters.CounterFactory()
# TODO(robertwb): Figure out the correct prefix to use for output counters
# from StateSampler.
state_sampler = statesampler.StateSampler(
'fnapi-step%s-' % descriptor.id, counter_factory)
consumers = collections.defaultdict(lambda: collections.defaultdict(list))
ops_by_id = {}
reversed_ops = []
for transform in reversed(descriptor.primitive_transform):
# TODO(robertwb): Figure out how to plumb through the operation name (e.g.
# "s3") from the service through the FnAPI so that msec counters can be
# reported and correctly plumbed through the service and the UI.
operation_name = 'fnapis%s' % transform.id
def only_element(iterable):
element, = iterable
return element
if transform.function_spec.urn == DATA_OUTPUT_URN:
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform.id,
name=only_element(transform.outputs.keys()))
op = DataOutputOperation(
operation_name,
transform.step_name,
consumers[transform.id],
counter_factory,
state_sampler,
coders[only_element(transform.outputs.values()).coder_reference],
target,
self.data_channel_factory.create_data_channel(
transform.function_spec))
elif transform.function_spec.urn == DATA_INPUT_URN:
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform.id,
name=only_element(transform.inputs.keys()))
op = DataInputOperation(
operation_name,
transform.step_name,
consumers[transform.id],
counter_factory,
state_sampler,
coders[only_element(transform.outputs.values()).coder_reference],
target,
self.data_channel_factory.create_data_channel(
transform.function_spec))
elif transform.function_spec.urn == PYTHON_DOFN_URN:
def create_side_input(tag, si):
# TODO(robertwb): Extract windows (and keys) out of element data.
return operation_specs.WorkerSideInputSource(
tag=tag,
source=SideInputSource(
self.state_handler,
beam_fn_api_pb2.StateKey(
function_spec_reference=si.view_fn.id),
coder=unpack_and_deserialize_py_fn(si.view_fn)))
output_tags = list(transform.outputs.keys())
spec = operation_specs.WorkerDoFn(
serialized_fn=unpack_function_spec_data(transform.function_spec),
output_tags=output_tags,
input=None,
side_inputs=[create_side_input(tag, si)
for tag, si in transform.side_inputs.items()],
output_coders=[coders[transform.outputs[out].coder_reference]
for out in output_tags])
op = operations.DoOperation(operation_name, spec, counter_factory,
state_sampler)
# TODO(robertwb): Move these to the constructor.
op.step_name = transform.step_name
for tag, op_consumers in consumers[transform.id].items():
for consumer in op_consumers:
op.add_receiver(
consumer, output_tags.index(tag))
elif transform.function_spec.urn == IDENTITY_DOFN_URN:
op = operations.FlattenOperation(operation_name, None, counter_factory,
state_sampler)
# TODO(robertwb): Move these to the constructor.
op.step_name = transform.step_name
for tag, op_consumers in consumers[transform.id].items():
for consumer in op_consumers:
op.add_receiver(consumer, 0)
elif transform.function_spec.urn == PYTHON_SOURCE_URN:
source = load_compressed(unpack_function_spec_data(
transform.function_spec))
# TODO(vikasrk): Remove this once custom source is implemented with
# splittable dofn via the data plane.
spec = operation_specs.WorkerRead(
iobase.SourceBundle(1.0, source, None, None),
[WindowedValueCoder(source.default_output_coder())])
op = operations.ReadOperation(operation_name, spec, counter_factory,
state_sampler)
op.step_name = transform.step_name
output_tags = list(transform.outputs.keys())
for tag, op_consumers in consumers[transform.id].items():
for consumer in op_consumers:
op.add_receiver(
consumer, output_tags.index(tag))
else:
raise NotImplementedError
# Record consumers.
for _, inputs in transform.inputs.items():
for target in inputs.target:
consumers[target.primitive_transform_reference][target.name].append(
op)
reversed_ops.append(op)
ops_by_id[transform.id] = op
return list(reversed(reversed_ops)), ops_by_id
