def to_runner_api_monitoring_info(self, name, transform_id): from apache_beam.metrics import monitoring_infos return monitoring_infos.int64_user_counter( name.namespace, name.name, metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=self.get_cumulative())), ptransform=transform_id)
def to_runner_api_monitoring_info(self): """Returns a Metric with this counter value for use in a MonitoringInfo.""" # TODO(ajamato): Update this code to be consistent with Gauges # and Distributions. Since there is no CounterData class this method # was added to CounterCell. Consider adding a CounterData class or # removing the GaugeData and DistributionData classes. return metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=self.get_cumulative()))
def int64_gauge(urn, metric, ptransform=None, tag=None): """Return the gauge monitoring info for the URN, metric and labels. Args: urn: The URN of the monitoring info/metric. metric: The metric proto field to use in the monitoring info. ptransform: The ptransform/step name used as a label. tag: The output tag name, used as a label. """ labels = create_labels(ptransform=ptransform, tag=tag) if isinstance(metric, int): metric = metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=metric)) return create_monitoring_info(urn, LATEST_INT64_TYPE, metric, labels)
def int64_counter(urn, metric, ptransform=None, tag=None): # type: (...) -> metrics_pb2.MonitoringInfo """Return the counter monitoring info for the specifed URN, metric and labels. Args: urn: The URN of the monitoring info/metric. metric: The metric proto field to use in the monitoring info. Or an int value. ptransform: The ptransform/step name used as a label. tag: The output tag name, used as a label. """ labels = create_labels(ptransform=ptransform, tag=tag) if isinstance(metric, int): metric = metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=metric)) return create_monitoring_info(urn, SUM_INT64_TYPE, metric, labels)
def to_runner_api_monitoring_info(self): """Returns a Metric with this value for use in a MonitoringInfo.""" return metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=self.value))
def distribution_combiner(metric_a, metric_b): a_data = metric_a.distribution_data.int_distribution_data b_data = metric_b.distribution_data.int_distribution_data return metrics_pb2.Metric(distribution_data=metrics_pb2.DistributionData( int_distribution_data=metrics_pb2.IntDistributionData( count=a_data.count + b_data.count, sum=a_data.sum + b_data.sum, min=min(a_data.min, b_data.min), max=max(a_data.max, b_data.max)))) _KNOWN_COMBINERS = { SUM_INT64_TYPE: lambda a, b: metrics_pb2.Metric(counter_data=metrics_pb2.CounterData( int64_value=a.counter_data.int64_value + b.counter_data.int64_value)), DISTRIBUTION_INT64_TYPE: distribution_combiner, } def max_timestamp(a, b): if a.ToNanoseconds() > b.ToNanoseconds(): return a else: return b def consolidate(metrics, key=to_key): grouped = collections.defaultdict(list) for metric in metrics:
def distribution_combiner(metric_a, metric_b): a_data = metric_a.distribution_data.int_distribution_data b_data = metric_b.distribution_data.int_distribution_data return metrics_pb2.Metric( distribution_data=metrics_pb2.DistributionData( int_distribution_data=metrics_pb2.IntDistributionData( count=a_data.count + b_data.count, sum=a_data.sum + b_data.sum, min=min(a_data.min, b_data.min), max=max(a_data.max, b_data.max)))) _KNOWN_COMBINERS = { SUM_INT64_TYPE: lambda a, b: metrics_pb2.Metric( counter_data=metrics_pb2.CounterData( int64_value=a.counter_data.int64_value + b.counter_data.int64_value) ), DISTRIBUTION_INT64_TYPE: distribution_combiner, } def max_timestamp(a, b): if a.ToNanoseconds() > b.ToNanoseconds(): return a else: return b def consolidate(metrics, key=to_key): grouped = collections.defaultdict(list) for metric in metrics: