def process_bundle(self, inputs, expected_outputs):
# Unique id for the instruction processing this bundle.
BundleManager._uid_counter += 1
process_bundle_id = 'bundle_%s' % BundleManager._uid_counter
# Register the bundle descriptor, if needed.
if self._registered:
registration_future = None
else:
process_bundle_registration = beam_fn_api_pb2.InstructionRequest(
register=beam_fn_api_pb2.RegisterRequest(
process_bundle_descriptor=[self._bundle_descriptor]))
registration_future = self._controller.control_handler.push(
process_bundle_registration)
self._registered = True
# Write all the input data to the channel.
for (transform_id, name), elements in inputs.items():
data_out = self._controller.data_plane_handler.output_stream(
process_bundle_id, beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id, name=name))
for element_data in elements:
data_out.write(element_data)
data_out.close()
# Actually start the bundle.
if registration_future and registration_future.get().error:
raise RuntimeError(registration_future.get().error)
process_bundle = beam_fn_api_pb2.InstructionRequest(
instruction_id=process_bundle_id,
process_bundle=beam_fn_api_pb2.ProcessBundleRequest(
process_bundle_descriptor_reference=self._bundle_descriptor.id))
result_future = self._controller.control_handler.push(process_bundle)
with ProgressRequester(
self._controller, process_bundle_id, self._progress_frequency):
# Gather all output data.
expected_targets = [
beam_fn_api_pb2.Target(primitive_transform_reference=transform_id,
name=output_name)
for (transform_id, output_name), _ in expected_outputs.items()]
logging.debug('Gather all output data from %s.', expected_targets)
for output in self._controller.data_plane_handler.input_elements(
process_bundle_id,
expected_targets,
abort_callback=lambda: (result_future.is_done()
and result_future.get().error)):
target_tuple = (
output.target.primitive_transform_reference, output.target.name)
if target_tuple in expected_outputs:
self._get_buffer(expected_outputs[target_tuple]).append(output.data)
logging.debug('Wait for the bundle to finish.')
result = result_future.get()
if result.error:
raise RuntimeError(result.error)
return result
def _run_map_task(self, map_task, control_handler, state_handler,
data_plane_handler, data_operation_spec):
registration, sinks, input_data = self._map_task_registration(
map_task, state_handler, data_operation_spec)
control_handler.push(registration)
process_bundle = beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
process_bundle=beam_fn_api_pb2.ProcessBundleRequest(
process_bundle_descriptor_reference=registration.register.
process_bundle_descriptor[0].id))
for (transform_id, name), elements in input_data.items():
data_out = data_plane_handler.output_stream(
process_bundle.instruction_id,
beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id, name=name))
data_out.write(elements)
data_out.close()
control_handler.push(process_bundle)
while True:
result = control_handler.pull()
if result.instruction_id == process_bundle.instruction_id:
if result.error:
raise RuntimeError(result.error)
expected_targets = [
beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id,
name=output_name)
for (transform_id, output_name), _ in sinks.items()
]
for output in data_plane_handler.input_elements(
process_bundle.instruction_id, expected_targets):
target_tuple = (
output.target.primitive_transform_reference,
output.target.name)
if target_tuple not in sinks:
# Unconsumed output.
continue
sink_op = sinks[target_tuple]
coder = sink_op.output_coders[0]
input_stream = create_InputStream(output.data)
elements = []
while input_stream.size() > 0:
elements.append(coder.get_impl().decode_from_stream(
input_stream, True))
if not sink_op.write_windowed_values:
elements = [e.value for e in elements]
for e in elements:
sink_op.output_buffer.append(e)
return
def create(factory, transform_id, transform_proto, grpc_port, consumers):
# Timers are the one special case where we don't want to call the
# (unlabeled) operation.process() method, which we detect here.
# TODO(robertwb): Consider generalizing if there are any more cases.
output_pcoll = only_element(transform_proto.outputs.values())
output_consumers = only_element(consumers.values())
if (len(output_consumers) == 1
and isinstance(only_element(output_consumers), operations.DoOperation)):
do_op = only_element(output_consumers)
for tag, pcoll_id in do_op.timer_inputs.items():
if pcoll_id == output_pcoll:
output_consumers[:] = [TimerConsumer(tag, do_op)]
break
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id,
name=only_element(list(transform_proto.outputs.keys())))
if grpc_port.coder_id:
output_coder = factory.get_coder(grpc_port.coder_id)
else:
logging.error(
'Missing required coder_id on grpc_port for %s; '
'using deprecated fallback.',
transform_id)
output_coder = factory.get_only_output_coder(transform_proto)
return DataInputOperation(
transform_proto.unique_name,
transform_proto.unique_name,
consumers,
factory.counter_factory,
factory.state_sampler,
output_coder,
input_target=target,
data_channel=factory.data_channel_factory.create_data_channel(grpc_port))
def as_target(op_input):
input_op_index, input_output_index = op_input
input_op = map_task[input_op_index][1]
return {
'ignored_input_tag':
beam_fn_api_pb2.Target.List(target=[
beam_fn_api_pb2.Target(
primitive_transform_reference=transform_index_to_id[
input_op_index],
name=output_tags(input_op)[input_output_index])
])
}
def create(factory, transform_id, transform_proto, grpc_port, consumers):
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id,
name=only_element(transform_proto.outputs.keys()))
return DataInputOperation(
transform_proto.unique_name,
transform_proto.unique_name,
consumers,
factory.counter_factory,
factory.state_sampler,
factory.get_only_output_coder(transform_proto),
input_target=target,
data_channel=factory.data_channel_factory.create_data_channel(grpc_port))
def create(factory, transform_id, transform_proto, grpc_port, consumers):
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id,
name=only_element(transform_proto.inputs.keys()))
return DataOutputOperation(
transform_proto.unique_name,
transform_proto.unique_name,
consumers,
factory.counter_factory,
factory.state_sampler,
# TODO(robertwb): Perhaps this could be distinct from the input coder?
factory.get_only_input_coder(transform_proto),
target=target,
data_channel=factory.data_channel_factory.create_data_channel(grpc_port))
def _data_channel_test_one_direction(self, from_channel, to_channel):
def send(instruction_id, target, data):
stream = from_channel.output_stream(instruction_id, target)
stream.write(data)
stream.close()
target_1 = beam_fn_api_pb2.Target(primitive_transform_reference='1',
name='out')
target_2 = beam_fn_api_pb2.Target(primitive_transform_reference='2',
name='out')
# Single write.
send('0', target_1, 'abc')
self.assertEqual(list(to_channel.input_elements('0', [target_1])), [
beam_fn_api_pb2.Elements.Data(
instruction_reference='0', target=target_1, data='abc')
])
# Multiple interleaved writes to multiple instructions.
target_2 = beam_fn_api_pb2.Target(primitive_transform_reference='2',
name='out')
send('1', target_1, 'abc')
send('2', target_1, 'def')
self.assertEqual(list(to_channel.input_elements('1', [target_1])), [
beam_fn_api_pb2.Elements.Data(
instruction_reference='1', target=target_1, data='abc')
])
send('2', target_2, 'ghi')
self.assertEqual(
list(to_channel.input_elements('2', [target_1, target_2])), [
beam_fn_api_pb2.Elements.Data(
instruction_reference='2', target=target_1, data='def'),
beam_fn_api_pb2.Elements.Data(
instruction_reference='2', target=target_2, data='ghi')
])
def create(factory, transform_id, transform_proto, grpc_port, consumers):
target = beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id,
name=only_element(list(transform_proto.inputs.keys())))
if grpc_port.coder_id:
output_coder = factory.get_coder(grpc_port.coder_id)
else:
logging.error(
'Missing required coder_id on grpc_port for %s; '
'using deprecated fallback.',
transform_id)
output_coder = factory.get_only_input_coder(transform_proto)
return DataOutputOperation(
transform_proto.unique_name,
transform_proto.unique_name,
consumers,
factory.counter_factory,
factory.state_sampler,
output_coder,
target=target,
data_channel=factory.data_channel_factory.create_data_channel(grpc_port))
def run_stage(self, controller, pipeline_components, stage, pcoll_buffers,
safe_coders):
context = pipeline_context.PipelineContext(pipeline_components)
data_operation_spec = controller.data_operation_spec()
def extract_endpoints(stage):
# Returns maps of transform names to PCollection identifiers.
# Also mutates IO stages to point to the data data_operation_spec.
data_input = {}
data_side_input = {}
data_output = {}
for transform in stage.transforms:
if transform.spec.urn in (bundle_processor.DATA_INPUT_URN,
bundle_processor.DATA_OUTPUT_URN):
pcoll_id = transform.spec.payload
if transform.spec.urn == bundle_processor.DATA_INPUT_URN:
target = transform.unique_name, only_element(
transform.outputs)
data_input[target] = pcoll_id
elif transform.spec.urn == bundle_processor.DATA_OUTPUT_URN:
target = transform.unique_name, only_element(
transform.inputs)
data_output[target] = pcoll_id
else:
raise NotImplementedError
if data_operation_spec:
transform.spec.payload = data_operation_spec.SerializeToString(
)
else:
transform.spec.payload = ""
elif transform.spec.urn == urns.PARDO_TRANSFORM:
payload = proto_utils.parse_Bytes(
transform.spec.payload,
beam_runner_api_pb2.ParDoPayload)
for tag, si in payload.side_inputs.items():
data_side_input[transform.unique_name, tag] = (
'materialize:' + transform.inputs[tag],
beam.pvalue.SideInputData.from_runner_api(
si, None))
return data_input, data_side_input, data_output
logging.info('Running %s', stage.name)
logging.debug(' %s', stage)
data_input, data_side_input, data_output = extract_endpoints(stage)
process_bundle_descriptor = beam_fn_api_pb2.ProcessBundleDescriptor(
id=self._next_uid(),
transforms={
transform.unique_name: transform
for transform in stage.transforms
},
pcollections=dict(pipeline_components.pcollections.items()),
coders=dict(pipeline_components.coders.items()),
windowing_strategies=dict(
pipeline_components.windowing_strategies.items()),
environments=dict(pipeline_components.environments.items()))
process_bundle_registration = beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
register=beam_fn_api_pb2.RegisterRequest(
process_bundle_descriptor=[process_bundle_descriptor]))
process_bundle = beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
process_bundle=beam_fn_api_pb2.ProcessBundleRequest(
process_bundle_descriptor_reference=process_bundle_descriptor.
id))
# Write all the input data to the channel.
for (transform_id, name), pcoll_id in data_input.items():
data_out = controller.data_plane_handler.output_stream(
process_bundle.instruction_id,
beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id, name=name))
for element_data in pcoll_buffers[pcoll_id]:
data_out.write(element_data)
data_out.close()
# Store the required side inputs into state.
for (transform_id, tag), (pcoll_id, si) in data_side_input.items():
elements_by_window = _WindowGroupingBuffer(si)
for element_data in pcoll_buffers[pcoll_id]:
elements_by_window.append(element_data)
for window, elements_data in elements_by_window.items():
state_key = beam_fn_api_pb2.StateKey(
multimap_side_input=beam_fn_api_pb2.StateKey.
MultimapSideInput(ptransform_id=transform_id,
side_input_id=tag,
window=window))
controller.state_handler.blocking_append(
state_key, elements_data, process_bundle.instruction_id)
# Register and start running the bundle.
logging.debug('Register and start running the bundle')
controller.control_handler.push(process_bundle_registration)
controller.control_handler.push(process_bundle)
# Wait for the bundle to finish.
logging.debug('Wait for the bundle to finish.')
while True:
result = controller.control_handler.pull()
if result and result.instruction_id == process_bundle.instruction_id:
if result.error:
raise RuntimeError(result.error)
break
expected_targets = [
beam_fn_api_pb2.Target(primitive_transform_reference=transform_id,
name=output_name)
for (transform_id, output_name), _ in data_output.items()
]
# Gather all output data.
logging.debug('Gather all output data from %s.', expected_targets)
for output in controller.data_plane_handler.input_elements(
process_bundle.instruction_id, expected_targets):
target_tuple = (output.target.primitive_transform_reference,
output.target.name)
if target_tuple in data_output:
pcoll_id = data_output[target_tuple]
if pcoll_id.startswith('materialize:'):
# Just store the data chunks for replay.
pcoll_buffers[pcoll_id].append(output.data)
elif pcoll_id.startswith('group:'):
# This is a grouping write, create a grouping buffer if needed.
if pcoll_id not in pcoll_buffers:
original_gbk_transform = pcoll_id.split(':', 1)[1]
transform_proto = pipeline_components.transforms[
original_gbk_transform]
input_pcoll = only_element(
transform_proto.inputs.values())
output_pcoll = only_element(
transform_proto.outputs.values())
pre_gbk_coder = context.coders[
safe_coders[pipeline_components.
pcollections[input_pcoll].coder_id]]
post_gbk_coder = context.coders[
safe_coders[pipeline_components.
pcollections[output_pcoll].coder_id]]
windowing_strategy = context.windowing_strategies[
pipeline_components.pcollections[output_pcoll].
windowing_strategy_id]
pcoll_buffers[pcoll_id] = _GroupingBuffer(
pre_gbk_coder, post_gbk_coder, windowing_strategy)
pcoll_buffers[pcoll_id].append(output.data)
else:
# These should be the only two identifiers we produce for now,
# but special side input writes may go here.
raise NotImplementedError(pcoll_id)
return result
def _map_task_registration(self, map_task, state_handler,
data_operation_spec):
input_data = {}
runner_sinks = {}
transforms = []
transform_index_to_id = {}
# Maps coders to new coder objects and references.
coders = {}
def coder_id(coder):
if coder not in coders:
coders[coder] = beam_fn_api_pb2.Coder(
function_spec=sdk_worker.pack_function_spec_data(
json.dumps(coder.as_cloud_object()),
sdk_worker.PYTHON_CODER_URN,
id=self._next_uid()))
return coders[coder].function_spec.id
def output_tags(op):
return getattr(op, 'output_tags', ['out'])
def as_target(op_input):
input_op_index, input_output_index = op_input
input_op = map_task[input_op_index][1]
return {
'ignored_input_tag':
beam_fn_api_pb2.Target.List(target=[
beam_fn_api_pb2.Target(
primitive_transform_reference=transform_index_to_id[
input_op_index],
name=output_tags(input_op)[input_output_index])
])
}
def outputs(op):
return {
tag:
beam_fn_api_pb2.PCollection(coder_reference=coder_id(coder))
for tag, coder in zip(output_tags(op), op.output_coders)
}
for op_ix, (stage_name, operation) in enumerate(map_task):
transform_id = transform_index_to_id[op_ix] = self._next_uid()
if isinstance(operation, operation_specs.WorkerInMemoryWrite):
# Write this data back to the runner.
fn = beam_fn_api_pb2.FunctionSpec(
urn=sdk_worker.DATA_OUTPUT_URN, id=self._next_uid())
if data_operation_spec:
fn.data.Pack(data_operation_spec)
inputs = as_target(operation.input)
side_inputs = {}
runner_sinks[(transform_id, 'out')] = operation
elif isinstance(operation, operation_specs.WorkerRead):
# A Read is either translated to a direct injection of windowed values
# into the sdk worker, or an injection of the source object into the
# sdk worker as data followed by an SDF that reads that source.
if (isinstance(operation.source.source,
maptask_executor_runner.InMemorySource)
and isinstance(
operation.source.source.default_output_coder(),
WindowedValueCoder)):
output_stream = create_OutputStream()
element_coder = (operation.source.source.
default_output_coder().get_impl())
# Re-encode the elements in the nested context and
# concatenate them together
for element in operation.source.source.read(None):
element_coder.encode_to_stream(element, output_stream,
True)
target_name = self._next_uid()
input_data[(transform_id,
target_name)] = output_stream.get()
fn = beam_fn_api_pb2.FunctionSpec(
urn=sdk_worker.DATA_INPUT_URN, id=self._next_uid())
if data_operation_spec:
fn.data.Pack(data_operation_spec)
inputs = {target_name: beam_fn_api_pb2.Target.List()}
side_inputs = {}
else:
# Read the source object from the runner.
source_coder = beam.coders.DillCoder()
input_transform_id = self._next_uid()
output_stream = create_OutputStream()
source_coder.get_impl().encode_to_stream(
GlobalWindows.windowed_value(operation.source),
output_stream, True)
target_name = self._next_uid()
input_data[(input_transform_id,
target_name)] = output_stream.get()
input_ptransform = beam_fn_api_pb2.PrimitiveTransform(
id=input_transform_id,
function_spec=beam_fn_api_pb2.FunctionSpec(
urn=sdk_worker.DATA_INPUT_URN,
id=self._next_uid()),
# TODO(robertwb): Possible name collision.
step_name=stage_name + '/inject_source',
inputs={target_name: beam_fn_api_pb2.Target.List()},
outputs={
'out':
beam_fn_api_pb2.PCollection(
coder_reference=coder_id(source_coder))
})
if data_operation_spec:
input_ptransform.function_spec.data.Pack(
data_operation_spec)
transforms.append(input_ptransform)
# Read the elements out of the source.
fn = sdk_worker.pack_function_spec_data(
OLDE_SOURCE_SPLITTABLE_DOFN_DATA,
sdk_worker.PYTHON_DOFN_URN,
id=self._next_uid())
inputs = {
'ignored_input_tag':
beam_fn_api_pb2.Target.List(target=[
beam_fn_api_pb2.Target(
primitive_transform_reference=
input_transform_id,
name='out')
])
}
side_inputs = {}
elif isinstance(operation, operation_specs.WorkerDoFn):
fn = sdk_worker.pack_function_spec_data(
operation.serialized_fn,
sdk_worker.PYTHON_DOFN_URN,
id=self._next_uid())
inputs = as_target(operation.input)
# Store the contents of each side input for state access.
for si in operation.side_inputs:
assert isinstance(si.source, iobase.BoundedSource)
element_coder = si.source.default_output_coder()
view_id = self._next_uid()
# TODO(robertwb): Actually flesh out the ViewFn API.
side_inputs[si.tag] = beam_fn_api_pb2.SideInput(
view_fn=sdk_worker.serialize_and_pack_py_fn(
element_coder,
urn=sdk_worker.PYTHON_ITERABLE_VIEWFN_URN,
id=view_id))
# Re-encode the elements in the nested context and
# concatenate them together
output_stream = create_OutputStream()
for element in si.source.read(
si.source.get_range_tracker(None, None)):
element_coder.get_impl().encode_to_stream(
element, output_stream, True)
elements_data = output_stream.get()
state_key = beam_fn_api_pb2.StateKey.MultimapSideInput(
key=view_id)
state_handler.Clear(state_key)
state_handler.Append(state_key, elements_data)
elif isinstance(operation, operation_specs.WorkerFlatten):
fn = sdk_worker.pack_function_spec_data(
operation.serialized_fn,
sdk_worker.IDENTITY_DOFN_URN,
id=self._next_uid())
inputs = {
'ignored_input_tag':
beam_fn_api_pb2.Target.List(target=[
beam_fn_api_pb2.Target(
primitive_transform_reference=
transform_index_to_id[input_op_index],
name=output_tags(map_task[input_op_index]
[1])[input_output_index]) for
input_op_index, input_output_index in operation.inputs
])
}
side_inputs = {}
else:
raise TypeError(operation)
ptransform = beam_fn_api_pb2.PrimitiveTransform(
id=transform_id,
function_spec=fn,
step_name=stage_name,
inputs=inputs,
side_inputs=side_inputs,
outputs=outputs(operation))
transforms.append(ptransform)
process_bundle_descriptor = beam_fn_api_pb2.ProcessBundleDescriptor(
id=self._next_uid(),
coders=coders.values(),
primitive_transform=transforms)
return beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
register=beam_fn_api_pb2.RegisterRequest(
process_bundle_descriptor=[process_bundle_descriptor
])), runner_sinks, input_data
def run_stage(
self, controller, pipeline_components, stage, pcoll_buffers, safe_coders):
coders = pipeline_context.PipelineContext(pipeline_components).coders
data_operation_spec = controller.data_operation_spec()
def extract_endpoints(stage):
# Returns maps of transform names to PCollection identifiers.
# Also mutates IO stages to point to the data data_operation_spec.
data_input = {}
data_side_input = {}
data_output = {}
for transform in stage.transforms:
pcoll_id = transform.spec.payload
if transform.spec.urn in (bundle_processor.DATA_INPUT_URN,
bundle_processor.DATA_OUTPUT_URN):
if transform.spec.urn == bundle_processor.DATA_INPUT_URN:
target = transform.unique_name, only_element(transform.outputs)
data_input[target] = pcoll_id
elif transform.spec.urn == bundle_processor.DATA_OUTPUT_URN:
target = transform.unique_name, only_element(transform.inputs)
data_output[target] = pcoll_id
else:
raise NotImplementedError
if data_operation_spec:
transform.spec.payload = data_operation_spec.SerializeToString()
transform.spec.any_param.Pack(data_operation_spec)
else:
transform.spec.payload = ""
transform.spec.any_param.Clear()
return data_input, data_side_input, data_output
logging.info('Running %s', stage.name)
logging.debug(' %s', stage)
data_input, data_side_input, data_output = extract_endpoints(stage)
if data_side_input:
raise NotImplementedError('Side inputs.')
process_bundle_descriptor = beam_fn_api_pb2.ProcessBundleDescriptor(
id=self._next_uid(),
transforms={transform.unique_name: transform
for transform in stage.transforms},
pcollections=dict(pipeline_components.pcollections.items()),
coders=dict(pipeline_components.coders.items()),
windowing_strategies=dict(
pipeline_components.windowing_strategies.items()),
environments=dict(pipeline_components.environments.items()))
process_bundle_registration = beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
register=beam_fn_api_pb2.RegisterRequest(
process_bundle_descriptor=[process_bundle_descriptor]))
process_bundle = beam_fn_api_pb2.InstructionRequest(
instruction_id=self._next_uid(),
process_bundle=beam_fn_api_pb2.ProcessBundleRequest(
process_bundle_descriptor_reference=
process_bundle_descriptor.id))
# Write all the input data to the channel.
for (transform_id, name), pcoll_id in data_input.items():
data_out = controller.data_plane_handler.output_stream(
process_bundle.instruction_id, beam_fn_api_pb2.Target(
primitive_transform_reference=transform_id, name=name))
for element_data in pcoll_buffers[pcoll_id]:
data_out.write(element_data)
data_out.close()
# Register and start running the bundle.
controller.control_handler.push(process_bundle_registration)
controller.control_handler.push(process_bundle)
# Wait for the bundle to finish.
while True:
result = controller.control_handler.pull()
if result and result.instruction_id == process_bundle.instruction_id:
if result.error:
raise RuntimeError(result.error)
break
# Gather all output data.
expected_targets = [
beam_fn_api_pb2.Target(primitive_transform_reference=transform_id,
name=output_name)
for (transform_id, output_name), _ in data_output.items()]
for output in controller.data_plane_handler.input_elements(
process_bundle.instruction_id, expected_targets):
target_tuple = (
output.target.primitive_transform_reference, output.target.name)
if target_tuple in data_output:
pcoll_id = data_output[target_tuple]
if pcoll_id.startswith('materialize:'):
# Just store the data chunks for replay.
pcoll_buffers[pcoll_id].append(output.data)
elif pcoll_id.startswith('group:'):
# This is a grouping write, create a grouping buffer if needed.
if pcoll_id not in pcoll_buffers:
original_gbk_transform = pcoll_id.split(':', 1)[1]
transform_proto = pipeline_components.transforms[
original_gbk_transform]
input_pcoll = only_element(transform_proto.inputs.values())
output_pcoll = only_element(transform_proto.outputs.values())
pre_gbk_coder = coders[safe_coders[
pipeline_components.pcollections[input_pcoll].coder_id]]
post_gbk_coder = coders[safe_coders[
pipeline_components.pcollections[output_pcoll].coder_id]]
pcoll_buffers[pcoll_id] = _GroupingBuffer(
pre_gbk_coder, post_gbk_coder)
pcoll_buffers[pcoll_id].append(output.data)
else:
# These should be the only two identifiers we produce for now,
# but special side input writes may go here.
raise NotImplementedError(pcoll_id)
def create_execution_tree_from_fn_api(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.MultimapSideInput(
key=si.view_fn.id.encode('utf-8')),
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))
