def push(self, dataframes, scope_id=None):
for frame in dataframes:
period = tzutils.diff_seconds(frame.end, frame.start)
for type_, point in frame.iterpoints():
self._conn.append_point(type_, frame.start, period, point)
self._conn.commit()
def test_diff_seconds_negative_arg_aware_objects_on_summer_change(self):
one = datetime.datetime(2019,
3,
31,
1,
tzinfo=tz.gettz('Europe/Paris'))
two = datetime.datetime(2019,
3,
31,
3,
tzinfo=tz.gettz('Europe/Paris'))
self.assertEqual(tzutils.diff_seconds(two, one), 3600)
def fetch_all(self, metric_name, start, end, scope_id, q_filter=None):
"""Returns metrics to be valorized."""
scope_key = CONF.collect.scope_key
method = self.conf[metric_name]['extra_args']['aggregation_method']
query_function = self.conf[metric_name]['extra_args'].get(
'query_function')
range_function = self.conf[metric_name]['extra_args'].get(
'range_function')
groupby = self.conf[metric_name].get('groupby', [])
metadata = self.conf[metric_name].get('metadata', [])
period = tzutils.diff_seconds(end, start)
time = end
# The metric with the period
query = '{0}{{{1}="{2}"}}[{3}s]'.format(metric_name, scope_key,
scope_id, period)
# Applying the aggregation_method or the range_function on
# a Range Vector
if range_function is not None:
query = "{0}({1})".format(range_function, query)
else:
query = "{0}_over_time({1})".format(method, query)
# Applying the query_function
if query_function is not None:
query = "{0}({1})".format(query_function, query)
# Applying the aggregation_method on a Instant Vector
query = "{0}({1})".format(method, query)
# Filter by groupby and metadata
query = "{0} by ({1})".format(query, ', '.join(groupby + metadata))
try:
res = self._conn.get_instant(
query,
time.isoformat(),
)
except PrometheusResponseError as e:
raise CollectError(*e.args)
# If the query returns an empty dataset,
# return an empty list
if not res['data']['result']:
return []
formatted_resources = []
for item in res['data']['result']:
metadata, groupby, qty = self._format_data(
metric_name,
scope_key,
scope_id,
start,
end,
item,
)
formatted_resources.append(
dataframe.DataPoint(
self.conf[metric_name]['unit'],
qty,
0,
groupby,
metadata,
))
return formatted_resources
def test_diff_seconds_negative_arg_aware_objects(self):
one = datetime.datetime(2019, 1, 1, 1, 1, 30, tzinfo=tz.tzutc())
two = datetime.datetime(2019, 1, 1, 1, 1, tzinfo=tz.tzutc())
self.assertEqual(tzutils.diff_seconds(two, one), 30)
def test_diff_seconds_negative_arg_naive_objects(self):
one = datetime.datetime(2019, 1, 1, 1, 1, 30)
two = datetime.datetime(2019, 1, 1, 1, 1)
self.assertEqual(tzutils.diff_seconds(two, one), 30)
def test_diff_seconds_positive_arg_aware_objects(self):
one = datetime.datetime(2019, 1, 1, 1, 1, 30, tzinfo=tz.UTC)
two = datetime.datetime(2019, 1, 1, 1, 1, tzinfo=tz.UTC)
self.assertEqual(tzutils.diff_seconds(one, two), 30)