def _get_points_proto(points):
protos = []
for point in points:
proto = metrics_pb2.Point(
timestamp=utils.proto_ts_from_datetime(point.timestamp))
if isinstance(point.value, value.ValueLong):
proto.int64_value = int(point.value.value)
elif isinstance(point.value, value.ValueDouble):
proto.double_value = float(point.value.value)
elif isinstance(point.value, value.ValueDistribution):
proto.distribution_value.MergeFrom(
metrics_pb2.DistributionValue(
sum=point.value.sum,
count=point.value.count,
sum_of_squared_deviation=point.value.
sum_of_squared_deviation,
bucket_options=_get_bucket_options_proto(
point.value.bucket_options)
if point.value.bucket_options else None,
buckets=_get_buckets_proto(point.value.buckets)))
# TODO: handle SUMMARY metrics, #567
else: # pragma: NO COVER
raise TypeError('Unsupported metric type: {}'.format(
type(point.value)))
protos.append(proto)
return protos
def get_collector_point(metric_record: MetricRecord) -> metrics_pb2.Point:
# TODO: horrible hack to get original list of keys to then get the bound
# instrument
point = metrics_pb2.Point(timestamp=utils.proto_timestamp_from_time_ns(
metric_record.aggregator.last_update_timestamp))
if metric_record.metric.value_type == int:
point.int64_value = metric_record.aggregator.checkpoint
elif metric_record.metric.value_type == float:
point.double_value = metric_record.aggregator.checkpoint
else:
raise TypeError("Unsupported metric type: {}".format(
metric_record.metric.value_type))
return point
def test_export_view_data(self):
v_data = view_data_module.ViewData(view=VIDEO_SIZE_VIEW,
start_time=TEST_TIME_STR,
end_time=TEST_TIME_STR)
v_data.record(context=tag_map_module.TagMap(), value=2, timestamp=None)
view_data = [v_data]
view_data = [metric_utils.view_data_to_metric(view_data[0], TEST_TIME)]
handler = mock.Mock(spec=ocagent.ExportRpcHandler)
ocagent.StatsExporter(handler).export_metrics(view_data)
self.assertEqual(
handler.send.call_args[0][0].metrics[0].metric_descriptor,
metrics_pb2.MetricDescriptor(
name=VIDEO_SIZE_VIEW_NAME,
description='processed video size over time',
unit='By',
type=metrics_pb2.MetricDescriptor.CUMULATIVE_DISTRIBUTION,
label_keys=[metrics_pb2.LabelKey(key=FRONTEND_KEY)]))
self.assertEqual(
handler.send.call_args[0][0].metrics[0].timeseries[0],
metrics_pb2.TimeSeries(
start_timestamp=timestamp_pb2.Timestamp(seconds=1545699723,
nanos=4000),
label_values=[metrics_pb2.LabelValue(has_value=False)],
points=[
metrics_pb2.Point(
timestamp=timestamp_pb2.Timestamp(seconds=1545699723,
nanos=4000),
distribution_value=metrics_pb2.DistributionValue(
sum=2,
count=1,
bucket_options=metrics_pb2.DistributionValue.
BucketOptions(
explicit=metrics_pb2.DistributionValue.
BucketOptions.Explicit(
bounds=[16.0 * MiB, 256.0 * MiB])),
buckets=[
metrics_pb2.DistributionValue.Bucket(count=1),
metrics_pb2.DistributionValue.Bucket(),
metrics_pb2.DistributionValue.Bucket(),
]))
]))
def get_collector_point(metric_record: MetricRecord) -> metrics_pb2.Point:
point = metrics_pb2.Point(
timestamp=utils.proto_timestamp_from_time_ns(
metric_record.metric.bind(
metric_record.label_set
).last_update_timestamp
)
)
if metric_record.metric.value_type == int:
point.int64_value = metric_record.aggregator.checkpoint
elif metric_record.metric.value_type == float:
point.double_value = metric_record.aggregator.checkpoint
else:
raise TypeError(
"Unsupported metric type: {}".format(
metric_record.metric.value_type
)
)
return point