def process_bundle(self, request, instruction_id):
bundle_processor = self.bundle_processor_cache.get(
instruction_id, request.process_bundle_descriptor_id)
try:
with bundle_processor.state_handler.process_instruction_id(
instruction_id, request.cache_tokens):
with self.maybe_profile(instruction_id):
delayed_applications, requests_finalization = (
bundle_processor.process_bundle(instruction_id))
monitoring_infos = bundle_processor.monitoring_infos()
monitoring_infos.extend(self.state_cache_metrics_fn())
response = beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
residual_roots=delayed_applications,
metrics=bundle_processor.metrics(),
monitoring_infos=monitoring_infos,
requires_finalization=requests_finalization))
# Don't release here if finalize is needed.
if not requests_finalization:
self.bundle_processor_cache.release(instruction_id)
return response
except: # pylint: disable=broad-except
# Don't re-use bundle processors on failure.
self.bundle_processor_cache.discard(instruction_id)
raise
def process_bundle(self, request, instruction_id):
bundle_processor.BundleProcessor(
self.fns[request.process_bundle_descriptor_reference],
self.state_handler,
self.data_channel_factory).process_bundle(instruction_id)
return beam_fn_api_pb2.ProcessBundleResponse()
def process_bundle(self, request, instruction_id):
ops = self.create_execution_tree(
self.fns[request.process_bundle_descriptor_reference])
expected_inputs = []
for op in ops:
if isinstance(op, DataOutputOperation):
# TODO(robertwb): Is there a better way to pass the instruction id to
# the operation?
op.set_output_stream(
op.data_channel.output_stream(instruction_id, op.target))
elif isinstance(op, DataInputOperation):
# We must wait until we receive "end of stream" for each of these ops.
expected_inputs.append(op)
# Start all operations.
for op in reversed(ops):
logging.info('start %s', op)
op.start()
# Inject inputs from data plane.
for input_op in expected_inputs:
for data in input_op.data_channel.input_elements(
instruction_id, [input_op.target]):
# ignores input name
input_op.process_encoded(data.data)
# Finish all operations.
for op in ops:
logging.info('finish %s', op)
op.finish()
return beam_fn_api_pb2.ProcessBundleResponse()
def process_bundle(
self,
inputs, # type: Mapping[str, execution.PartitionableBuffer]
expected_outputs, # type: DataOutput
fired_timers, # type: Mapping[Tuple[str, str], execution.PartitionableBuffer]
expected_output_timers, # type: Dict[Tuple[str, str], str]
dry_run=False,
):
# type: (...) -> BundleProcessResult
part_inputs = [{} for _ in range(self._num_workers)
] # type: List[Dict[str, List[bytes]]]
# Timers are only executed on the first worker
# TODO(BEAM-9741): Split timers to multiple workers
timer_inputs = [
fired_timers if i == 0 else {} for i in range(self._num_workers)
]
for name, input in inputs.items():
for ix, part in enumerate(input.partition(self._num_workers)):
part_inputs[ix][name] = part
merged_result = None # type: Optional[beam_fn_api_pb2.InstructionResponse]
split_result_list = [
] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
def execute(part_map_input_timers):
# type: (...) -> BundleProcessResult
part_map, input_timers = part_map_input_timers
bundle_manager = BundleManager(
self.bundle_context_manager,
self._progress_frequency,
cache_token_generator=self._cache_token_generator)
return bundle_manager.process_bundle(part_map, expected_outputs,
input_timers,
expected_output_timers,
dry_run)
with thread_pool_executor.shared_unbounded_instance() as executor:
for result, split_result in executor.map(
execute,
zip(
part_inputs, # pylint: disable=zip-builtin-not-iterating
timer_inputs)):
split_result_list += split_result
if merged_result is None:
merged_result = result
else:
merged_result = beam_fn_api_pb2.InstructionResponse(
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
monitoring_infos=monitoring_infos.consolidate(
itertools.chain(
result.process_bundle.monitoring_infos,
merged_result.process_bundle.
monitoring_infos))),
error=result.error or merged_result.error)
assert merged_result is not None
return merged_result, split_result_list
def process_bundle(self, request, instruction_id):
with self.get_bundle_processor(
instruction_id, request.process_bundle_descriptor_reference
) as bundle_processor:
bundle_processor.process_bundle(instruction_id)
return beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
metrics=bundle_processor.metrics(),
monitoring_infos=bundle_processor.monitoring_infos()))
def merge_results(last_result):
""" Merge the latest result with other accumulated results. """
return (
last_result if final_result is None else
beam_fn_api_pb2.InstructionResponse(
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
monitoring_infos=monitoring_infos.consolidate(
itertools.chain(
final_result.process_bundle.monitoring_infos,
last_result.process_bundle.monitoring_infos))),
error=final_result.error or last_result.error))
def process_bundle(self, request, instruction_id):
with self.get_bundle_processor(
instruction_id, request.process_bundle_descriptor_reference
) as bundle_processor:
with self.maybe_profile(instruction_id):
delayed_applications = bundle_processor.process_bundle(
instruction_id)
return beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
residual_roots=delayed_applications,
metrics=bundle_processor.metrics(),
monitoring_infos=bundle_processor.monitoring_infos()))
def process_bundle(self, request, instruction_id):
self.bundle_processors[
instruction_id] = processor = bundle_processor.BundleProcessor(
self.fns[request.process_bundle_descriptor_reference],
self.state_handler, self.data_channel_factory)
try:
processor.process_bundle(instruction_id)
finally:
del self.bundle_processors[instruction_id]
return beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
metrics=processor.metrics()))
def process_bundle(self,
inputs, # type: Mapping[str, PartitionableBuffer]
expected_outputs, # type: DataOutput
fired_timers, # type: Mapping[Tuple[str, str], PartitionableBuffer]
expected_output_timers # type: Dict[Tuple[str, str], str]
):
# type: (...) -> BundleProcessResult
part_inputs = [{} for _ in range(self._num_workers)
] # type: List[Dict[str, List[bytes]]]
for name, input in inputs.items():
for ix, part in enumerate(input.partition(self._num_workers)):
part_inputs[ix][name] = part
merged_result = None # type: Optional[beam_fn_api_pb2.InstructionResponse]
split_result_list = [
] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
def execute(part_map):
# type: (...) -> BundleProcessResult
bundle_manager = BundleManager(
self._worker_handler_list,
self._get_buffer,
self._get_input_coder_impl,
self._bundle_descriptor,
self._progress_frequency,
self._registered,
cache_token_generator=self._cache_token_generator)
return bundle_manager.process_bundle(
part_map, expected_outputs, fired_timers, expected_output_timers)
with UnboundedThreadPoolExecutor() as executor:
for result, split_result in executor.map(execute, part_inputs):
split_result_list += split_result
if merged_result is None:
merged_result = result
else:
merged_result = beam_fn_api_pb2.InstructionResponse(
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
monitoring_infos=monitoring_infos.consolidate(
itertools.chain(
result.process_bundle.monitoring_infos,
merged_result.process_bundle.monitoring_infos))),
error=result.error or merged_result.error)
assert merged_result is not None
return merged_result, split_result_list
def process_bundle(self, request, instruction_id):
process_bundle_desc = self.fns[
request.process_bundle_descriptor_reference]
state_handler = self.state_handler_factory.create_state_handler(
process_bundle_desc.state_api_service_descriptor)
self.bundle_processors[
instruction_id] = processor = bundle_processor.BundleProcessor(
process_bundle_desc, state_handler, self.data_channel_factory)
try:
with state_handler.process_instruction_id(instruction_id):
processor.process_bundle(instruction_id)
finally:
del self.bundle_processors[instruction_id]
return beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
metrics=processor.metrics(),
monitoring_infos=processor.monitoring_infos()))
def process_bundle(self, request, instruction_id):
bundle_processor = self.bundle_processor_cache.get(
instruction_id, request.process_bundle_descriptor_reference)
try:
with bundle_processor.state_handler.process_instruction_id(
instruction_id):
with self.maybe_profile(instruction_id):
delayed_applications = bundle_processor.process_bundle(
instruction_id)
response = beam_fn_api_pb2.InstructionResponse(
instruction_id=instruction_id,
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
residual_roots=delayed_applications,
metrics=bundle_processor.metrics(),
monitoring_infos=bundle_processor.monitoring_infos(
)))
# TODO(boyuanz): Don't release here if finalize is needed.
self.bundle_processor_cache.release(instruction_id)
return response
except: # pylint: disable=broad-except
# Don't re-use bundle processors on failure.
self.bundle_processor_cache.discard(instruction_id)
raise
def _run_stage(self,
runner_execution_context, # type: execution.FnApiRunnerExecutionContext
bundle_context_manager, # type: execution.BundleContextManager
):
# type: (...) -> beam_fn_api_pb2.InstructionResponse
"""Run an individual stage.
Args:
runner_execution_context (execution.FnApiRunnerExecutionContext): An
object containing execution information for the pipeline.
stage (translations.Stage): A description of the stage to execute.
"""
worker_handler_list = bundle_context_manager.worker_handlers
worker_handler_manager = runner_execution_context.worker_handler_manager
_LOGGER.info('Running %s', bundle_context_manager.stage.name)
(data_input, data_side_input, data_output,
expected_timer_output) = self._extract_endpoints(
bundle_context_manager, runner_execution_context)
worker_handler_manager.register_process_bundle_descriptor(
bundle_context_manager.process_bundle_descriptor)
# Store the required side inputs into state so it is accessible for the
# worker when it runs this bundle.
self._store_side_inputs_in_state(runner_execution_context, data_side_input)
# Change cache token across bundle repeats
cache_token_generator = FnApiRunner.get_cache_token_generator(static=False)
self._run_bundle_multiple_times_for_testing(
runner_execution_context,
bundle_context_manager,
data_input,
data_output, {},
expected_timer_output,
cache_token_generator=cache_token_generator)
bundle_manager = ParallelBundleManager(
worker_handler_list,
bundle_context_manager.get_buffer,
bundle_context_manager.get_input_coder_impl,
bundle_context_manager.process_bundle_descriptor,
self._progress_frequency,
num_workers=self._num_workers,
cache_token_generator=cache_token_generator)
# For the first time of processing, we don't have fired timers as inputs.
result, splits = bundle_manager.process_bundle(data_input,
data_output,
{},
expected_timer_output)
last_result = result
last_sent = data_input
# We cannot split deferred_input until we include residual_roots to
# merged results. Without residual_roots, pipeline stops earlier and we
# may miss some data.
# We also don't partition fired timer inputs for the same reason.
bundle_manager._num_workers = 1
while True:
deferred_inputs = {} # type: Dict[str, PartitionableBuffer]
fired_timers = {}
self._collect_written_timers_and_add_to_fired_timers(
bundle_context_manager, fired_timers)
# Queue any process-initiated delayed bundle applications.
for delayed_application in last_result.process_bundle.residual_roots:
name = bundle_context_manager.input_for(
delayed_application.application.transform_id,
delayed_application.application.input_id)
if name not in deferred_inputs:
deferred_inputs[name] = ListBuffer(
coder_impl=bundle_context_manager.get_input_coder_impl(name))
deferred_inputs[name].append(delayed_application.application.element)
# Queue any runner-initiated delayed bundle applications.
self._add_residuals_and_channel_splits_to_deferred_inputs(
splits, bundle_context_manager, last_sent, deferred_inputs)
if deferred_inputs or fired_timers:
# The worker will be waiting on these inputs as well.
for other_input in data_input:
if other_input not in deferred_inputs:
deferred_inputs[other_input] = ListBuffer(
coder_impl=bundle_context_manager.get_input_coder_impl(
other_input))
# TODO(robertwb): merge results
last_result, splits = bundle_manager.process_bundle(
deferred_inputs, data_output, fired_timers, expected_timer_output)
last_sent = deferred_inputs
result = beam_fn_api_pb2.InstructionResponse(
process_bundle=beam_fn_api_pb2.ProcessBundleResponse(
monitoring_infos=monitoring_infos.consolidate(
itertools.chain(
result.process_bundle.monitoring_infos,
last_result.process_bundle.monitoring_infos))),
error=result.error or last_result.error)
else:
break
return result
