def _run_read_from(self, transform_node, source):
"""Used when this operation is the result of reading source."""
if not isinstance(source, NativeSource):
source = iobase.SourceBundle(1.0, source, None, None)
output = transform_node.outputs[None]
element_coder = self._get_coder(output)
read_op = operation_specs.WorkerRead(source, output_coders=[element_coder])
self.outputs[output] = len(self.map_tasks), 0, 0
self.map_tasks.append([(transform_node.full_label, read_op)])
return len(self.map_tasks) - 1
def create(factory, transform_id, transform_proto, parameter, consumers):
source = pickler.loads(parameter.value)
spec = operation_specs.WorkerRead(
iobase.SourceBundle(1.0, source, None, None),
[WindowedValueCoder(source.default_output_coder())])
return factory.augment_oldstyle_op(
operations.ReadOperation(transform_proto.unique_name, spec,
factory.counter_factory,
factory.state_sampler),
transform_proto.unique_name, consumers)
def create(factory, transform_id, transform_proto, parameter, consumers):
# The Dataflow runner harness strips the base64 encoding.
source = pickler.loads(base64.b64encode(parameter))
spec = operation_specs.WorkerRead(
iobase.SourceBundle(1.0, source, None, None),
[WindowedValueCoder(source.default_output_coder())])
return factory.augment_oldstyle_op(
operations.ReadOperation(transform_proto.unique_name, spec,
factory.counter_factory,
factory.state_sampler),
transform_proto.unique_name, consumers)
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
def run_ParDo(self, transform_node):
transform = transform_node.transform
output = transform_node.outputs[None]
element_coder = self._get_coder(output)
map_task_index, producer_index, output_index = self.outputs[
transform_node.inputs[0]]
# If any of this ParDo's side inputs depend on outputs from this map_task,
# we can't continue growing this map task.
def is_reachable(leaf, root):
if leaf == root:
return True
else:
return any(
is_reachable(x, root) for x in self.dependencies[leaf])
if any(
is_reachable(self.outputs[side_input.pvalue][0],
map_task_index)
for side_input in transform_node.side_inputs):
# Start a new map tasks.
input_element_coder = self._get_coder(transform_node.inputs[0])
output_buffer = OutputBuffer(input_element_coder)
fusion_break_write = operation_specs.WorkerInMemoryWrite(
output_buffer=output_buffer,
write_windowed_values=True,
input=(producer_index, output_index),
output_coders=[input_element_coder])
self.map_tasks[map_task_index].append(
(transform_node.full_label + '/Write', fusion_break_write))
original_map_task_index = map_task_index
map_task_index, producer_index, output_index = len(
self.map_tasks), 0, 0
fusion_break_read = operation_specs.WorkerRead(
output_buffer.source_bundle(),
output_coders=[input_element_coder])
self.map_tasks.append([(transform_node.full_label + '/Read',
fusion_break_read)])
self.dependencies[map_task_index].add(original_map_task_index)
def create_side_read(side_input):
label = self.side_input_labels[side_input]
output_buffer = self.run_side_write(
side_input.pvalue,
'%s/%s' % (transform_node.full_label, label))
return operation_specs.WorkerSideInputSource(
output_buffer.source(), label)
do_op = operation_specs.WorkerDoFn( #
serialized_fn=pickler.dumps(
DataflowRunner._pardo_fn_data(
transform_node,
lambda side_input: self.side_input_labels[side_input])),
output_tags=[PropertyNames.OUT] + [
'%s_%s' % (PropertyNames.OUT, tag)
for tag in transform.output_tags
],
# Same assumption that DataflowRunner has about coders being compatible
# across outputs.
output_coders=[element_coder] * (len(transform.output_tags) + 1),
input=(producer_index, output_index),
side_inputs=[
create_side_read(side_input)
for side_input in transform_node.side_inputs
])
producer_index = len(self.map_tasks[map_task_index])
self.outputs[transform_node.outputs[None]] = (map_task_index,
producer_index, 0)
for ix, tag in enumerate(transform.output_tags):
self.outputs[transform_node.
outputs[tag]] = map_task_index, producer_index, ix + 1
self.map_tasks[map_task_index].append(
(transform_node.full_label, do_op))
for side_input in transform_node.side_inputs:
self.dependencies[map_task_index].add(
self.outputs[side_input.pvalue][0])
