def test_send(self):
m = monitors.Monitor()
metric1 = metrics_pb2.MetricsPayload()
with self.assertRaises(NotImplementedError):
m.send(metric1)
with self.assertRaises(NotImplementedError):
m.failed()
def test_send(self):
m = monitors.NullMonitor()
payload = metrics_pb2.MetricsPayload()
payload.metrics_collection.add().metrics_data_set.add(
).metric_name = 'm1'
m.send(payload)
self.assertFalse(m.failed())
def test_send_log(self, mock_logging_info):
m = monitors.DebugMonitor()
payload = metrics_pb2.MetricsPayload()
payload.metrics_collection.add().metrics_data_set.add().metric_name = 'm1'
m.send(payload)
self.assertEqual(1, mock_logging_info.call_count)
output = mock_logging_info.call_args[0][1]
self.assertIn('metrics_data_set {\n metric_name: "m1"\n }', output)
def _generate_proto():
"""Generate MetricsPayload for global_monitor.send()."""
proto = metrics_pb2.MetricsPayload()
# Key: Target, value: MetricsCollection.
collections = {}
# Key: (Target, metric name) tuple, value: MetricsDataSet.
data_sets = {}
count = 0
for (target, metric, start_time, end_time,
fields_values) in state.store.get_all():
for fields, value in fields_values.iteritems():
if count >= METRICS_DATA_LENGTH_LIMIT:
yield proto
proto = metrics_pb2.MetricsPayload()
collections.clear()
data_sets.clear()
count = 0
if target not in collections:
collections[target] = proto.metrics_collection.add()
target.populate_target_pb(collections[target])
collection = collections[target]
key = (target, metric.name)
new_data_set = None
if key not in data_sets:
new_data_set = metrics_pb2.MetricsDataSet()
metric.populate_data_set(new_data_set)
data = metrics_pb2.MetricsData()
metric.populate_data(data, start_time, end_time, fields, value)
# All required data protos have been successfully populated. Now we can
# insert them in serialized proto and bookeeping data structures.
if new_data_set is not None:
collection.metrics_data_set.add().CopyFrom(new_data_set)
data_sets[key] = collection.metrics_data_set[-1]
data_sets[key].data.add().CopyFrom(data)
count += 1
if count > 0:
yield proto
def test_send_file(self):
with infra_libs.temporary_directory() as temp_dir:
filename = os.path.join(temp_dir, 'out')
m = monitors.DebugMonitor(filename)
payload = metrics_pb2.MetricsPayload()
payload.metrics_collection.add().metrics_data_set.add().metric_name = 'm1'
m.send(payload)
with open(filename) as fh:
output = fh.read()
self.assertIn('metrics_data_set {\n metric_name: "m1"\n }', output)
def test_send_http_failure(self, _load_creds):
mon = monitors.HttpsMonitor('endpoint',
monitors.CredentialFactory.from_string('/path/to/creds.p8.json'))
mon._http.request = mock.MagicMock(side_effect=ValueError())
metric1 = metrics_pb2.MetricsPayload()
metric1.metrics_collection.add().metrics_data_set.add().metric_name = 'a'
mon.send(metric1)
mon._http.request.assert_called_once_with(
'endpoint',
method='POST',
body=self.message(metric1),
headers={'Content-Type': 'application/json'})
def _test_send(self, http):
mon = monitors.HttpsMonitor('endpoint',
monitors.CredentialFactory.from_string(':gce'), http=http)
resp = mock.MagicMock(spec=httplib2.Response, status=200)
mon._http.request = mock.MagicMock(return_value=[resp, ""])
payload = metrics_pb2.MetricsPayload()
payload.metrics_collection.add().metrics_data_set.add().metric_name = 'a'
mon.send(payload)
mon._http.request.assert_has_calls([
mock.call('endpoint', method='POST', body=self.message(payload),
headers={'Content-Type': 'application/json'}),
])
def test_send_resp_failure(self, _load_creds):
mon = monitors.HttpsMonitor('endpoint',
monitors.CredentialFactory.from_string('/path/to/creds.p8.json'))
resp = mock.MagicMock(spec=httplib2.Response, status=400)
mon._http.request = mock.MagicMock(return_value=[resp, ""])
metric1 = metrics_pb2.MetricsPayload()
metric1.metrics_collection.add().metrics_data_set.add().metric_name = 'a'
mon.send(metric1)
mon._http.request.assert_called_once_with(
'endpoint',
method='POST',
body=self.message(metric1),
headers={'Content-Type': 'application/json'})
def _generate_proto():
"""Generate MetricsPayload for global_monitor.send()."""
proto = metrics_pb2.MetricsPayload()
# Key: Target, value: MetricsCollection.
collections = {} # pylint: disable=redefined-outer-name
# Key: (Target, metric name) tuple, value: MetricsDataSet.
data_sets = {}
count = 0
for (target, metric, start_times, end_time,
fields_values) in state.store.get_all():
for fields, value in six.iteritems(fields_values):
# In default, the start time of all data points for a single stream
# should be set with the first time of a value change in the stream,
# until metric.reset() invoked.
#
# e.g.,
# At 00:00.
# {value: 1,
# fields: ('metric:result': 'success', 'metric:command': 'get_name'),
# start_timestamp=0, end_timestamp=0}
#
# At 00:01.
# {value: 1,
# fields: ('metric:result': 'success', 'metric:command': 'get_name'),
# start_timestamp=0, end_timestamp=1}
#
# At 00:02.
# {value: 2,
# fields: ('metric:result': 'success', 'metric:command': 'get_name'),
# start_timestamp=0, end_timestamp=2}
#
# This is important for cumulative metrics, because the monitoring
# backend detects the restart of a monitoring target and inserts a reset
# point to make Delta()/Rate() computation results accurate.
# If a given metric has own start_time, which can be set via
# metric.dangerously_set_start_time(), then report all the data points
# with the metric-level start_time.
#
# Otherwise, report data points with the first value change time.
start_time = metric.start_time or start_times.get(fields, end_time)
if count >= METRICS_DATA_LENGTH_LIMIT:
yield proto
proto = metrics_pb2.MetricsPayload()
collections.clear()
data_sets.clear()
count = 0
if target not in collections:
collections[target] = proto.metrics_collection.add()
if isinstance(target, tuple):
_populate_root_labels(collections[target].root_labels,
target)
else:
target.populate_target_pb(collections[target])
collection = collections[target]
key = (target, metric.name)
new_data_set = None
if key not in data_sets:
new_data_set = metrics_pb2.MetricsDataSet()
metric.populate_data_set(new_data_set)
data = metrics_pb2.MetricsData()
metric.populate_data(data, start_time, end_time, fields, value)
# All required data protos have been successfully populated. Now we can
# insert them in serialized proto and bookeeping data structures.
if new_data_set is not None:
collection.metrics_data_set.add().CopyFrom(new_data_set)
data_sets[key] = collection.metrics_data_set[-1]
data_sets[key].data.add().CopyFrom(data)
count += 1
if count > 0:
yield proto
