def export_metrics(self, metrics):
if metrics:
envelopes = []
for metric in metrics:
# No support for histogram aggregations
type_ = metric.descriptor.type
if type_ != MetricDescriptorType.CUMULATIVE_DISTRIBUTION:
md = metric.descriptor
# Each time series will be uniquely
# identified by it's label values
for time_series in metric.time_series:
# Using stats, time_series should
# only have one point which contains
# the aggregated value
data_point = self.create_data_points(time_series,
md)[0]
# The timestamp is when the metric was recorded
time_stamp = time_series.points[0].timestamp
# Get the properties using label keys from metric
# and label values of the time series
properties = self.create_properties(time_series, md)
envelopes.append(
self.create_envelope(data_point, time_stamp,
properties))
# Send data in batches of max_batch_size
if envelopes:
batched_envelopes = list(
common_utils.window(envelopes, self.max_batch_size))
for batch in batched_envelopes:
self._transmit_without_retry(batch)
def create_batched_time_series(self, view_data, batch_size):
""" Create the data structure that will be
sent to Stackdriver Monitoring
"""
time_series_list = itertools.chain.from_iterable(
self.create_time_series_list(v_data, self.options.resource,
self.options.metric_prefix)
for v_data in view_data)
return list(utils.window(time_series_list, batch_size))
def export_metrics(self, metrics):
envelopes = []
for metric in metrics:
envelopes.extend(self.metric_to_envelopes(metric))
# Send data in batches of max_batch_size
batched_envelopes = list(common_utils.window(
envelopes, self.max_batch_size))
for batch in batched_envelopes:
batch = self.apply_telemetry_processors(batch)
result = self._transmit(batch)
if result > 0:
self.storage.put(batch, result)
# If there is still room to transmit envelopes, transmit from storage
# if available
if len(envelopes) < self.options.max_batch_size:
self._transmit_from_storage()
def create_batched_time_series(self,
metrics,
batch_size=MAX_TIME_SERIES_PER_UPLOAD):
time_series_list = itertools.chain.from_iterable(
self.create_time_series_list(metric) for metric in metrics)
return list(utils.window(time_series_list, batch_size))
