def test_constructor_defaults(self):
name = "testName"
description = "testMeasure"
measure = measure_module.BaseMeasure(name=name, description=description)
self.assertEqual(None, measure.unit)
def test_constructor_explicit(self):
name = "testName"
description = "testMeasure"
unit = "testUnit"
measure = measure_module.BaseMeasure(name=name, description=description, unit=unit)
self.assertEqual("testName", measure.name)
self.assertEqual("testMeasure", measure.description)
self.assertEqual("testUnit", measure.unit)
def _record_metrics(self, metrics):
# The list of view data is what we are going to use for the
# final export to exporter.
view_data_changed: List[ViewData] = []
# Walk the protobufs and convert them to ViewData
for metric in metrics:
descriptor = metric.metric_descriptor
timeseries = metric.timeseries
if len(timeseries) == 0:
continue
columns = [label_key.key for label_key in descriptor.label_keys]
start_time = timeseries[0].start_timestamp.seconds
# Create the view and view_data
measure = measure_module.BaseMeasure(
descriptor.name, descriptor.description, descriptor.unit)
view = self.view_manager.measure_to_view_map.get_view(
descriptor.name, None)
if not view:
view = View(
descriptor.name,
descriptor.description,
columns,
measure,
aggregation=None)
self.view_manager.measure_to_view_map.register_view(
view, start_time)
view_data = (self.view_manager.measure_to_view_map.
_measure_to_view_data_list_map[measure.name][-1])
view_data_changed.append(view_data)
# Create the aggregation and fill it in the our stats
for series in timeseries:
tag_vals = tuple(val.value for val in series.label_values)
for point in series.points:
if point.HasField("int64_value"):
data = CountAggregationData(point.int64_value)
elif point.HasField("double_value"):
data = LastValueAggregationData(
ValueDouble, point.double_value)
elif point.HasField("distribution_value"):
dist_value = point.distribution_value
counts_per_bucket = [
bucket.count for bucket in dist_value.buckets
]
bucket_bounds = (
dist_value.bucket_options.explicit.bounds)
data = DistributionAggregationData(
dist_value.sum / dist_value.count,
dist_value.count,
dist_value.sum_of_squared_deviation,
counts_per_bucket, bucket_bounds)
else:
raise ValueError("Summary is not supported")
view_data.tag_value_aggregation_data_map[tag_vals] = data
# Finally, export all the values
self.view_manager.measure_to_view_map.export(view_data_changed)