本文整理匯總了Python中apache_beam.metrics.execution.MetricsContainer.reset方法的典型用法代碼示例。如果您正苦於以下問題:Python MetricsContainer.reset方法的具體用法?Python MetricsContainer.reset怎麽用?Python MetricsContainer.reset使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類apache_beam.metrics.execution.MetricsContainer
的用法示例。
在下文中一共展示了MetricsContainer.reset方法的1個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: Operation
# 需要導入模塊: from apache_beam.metrics.execution import MetricsContainer [as 別名]
# 或者: from apache_beam.metrics.execution.MetricsContainer import reset [as 別名]
class Operation(object):
"""An operation representing the live version of a work item specification.
An operation can have one or more outputs and for each output it can have
one or more receiver operations that will take that as input.
"""
def __init__(self, name_context, spec, counter_factory, state_sampler):
"""Initializes a worker operation instance.
Args:
name_context: A NameContext instance or string(deprecated), with the
name information for this operation.
spec: A operation_specs.Worker* instance.
counter_factory: The CounterFactory to use for our counters.
state_sampler: The StateSampler for the current operation.
"""
if isinstance(name_context, common.NameContext):
# TODO(BEAM-4028): Clean this up once it's completely migrated.
# We use the specific operation name that is used for metrics and state
# sampling.
self.name_context = name_context
else:
self.name_context = common.NameContext(name_context)
self.spec = spec
self.counter_factory = counter_factory
self.execution_context = None
self.consumers = collections.defaultdict(list)
# These are overwritten in the legacy harness.
self.metrics_container = MetricsContainer(self.name_context.metrics_name())
self.state_sampler = state_sampler
self.scoped_start_state = self.state_sampler.scoped_state(
self.name_context, 'start', metrics_container=self.metrics_container)
self.scoped_process_state = self.state_sampler.scoped_state(
self.name_context, 'process', metrics_container=self.metrics_container)
self.scoped_finish_state = self.state_sampler.scoped_state(
self.name_context, 'finish', metrics_container=self.metrics_container)
# TODO(ccy): the '-abort' state can be added when the abort is supported in
# Operations.
self.receivers = []
# Legacy workers cannot call setup() until after setting additional state
# on the operation.
self.setup_done = False
def setup(self):
with self.scoped_start_state:
self.debug_logging_enabled = logging.getLogger().isEnabledFor(
logging.DEBUG)
# Everything except WorkerSideInputSource, which is not a
# top-level operation, should have output_coders
#TODO(pabloem): Define better what step name is used here.
if getattr(self.spec, 'output_coders', None):
self.receivers = [
ConsumerSet.create(
self.counter_factory,
self.name_context.logging_name(),
i,
self.consumers[i], coder)
for i, coder in enumerate(self.spec.output_coders)]
self.setup_done = True
def start(self):
"""Start operation."""
if not self.setup_done:
# For legacy workers.
self.setup()
def process(self, o):
"""Process element in operation."""
pass
def try_split(self, fraction_of_remainder):
return None
def finish(self):
"""Finish operation."""
pass
def reset(self):
self.metrics_container.reset()
def output(self, windowed_value, output_index=0):
cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)
def add_receiver(self, operation, output_index=0):
"""Adds a receiver operation for the specified output."""
self.consumers[output_index].append(operation)
def progress_metrics(self):
return beam_fn_api_pb2.Metrics.PTransform(
processed_elements=beam_fn_api_pb2.Metrics.PTransform.ProcessedElements(
measured=beam_fn_api_pb2.Metrics.PTransform.Measured(
total_time_spent=(
self.scoped_start_state.sampled_seconds()
+ self.scoped_process_state.sampled_seconds()
+ self.scoped_finish_state.sampled_seconds()),
# Multi-output operations should override this.
#.........這裏部分代碼省略.........