#
# Copyright 2015 eNovance
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from gnocchiclient import client
from oslo_log import log
from oslo_serialization import jsonutils
from aodh.evaluator import threshold
from aodh.i18n import _LW
from aodh import keystone_client
LOG = log.getLogger(__name__)
# The list of points that Gnocchi API returned is composed
# of tuples with (timestamp, granularity, value)
GRANULARITY = 1
VALUE = 2
[docs]class GnocchiBase(threshold.ThresholdEvaluator):
def __init__(self, conf):
super(GnocchiBase, self).__init__(conf)
self._gnocchi_client = client.Client(
'1', keystone_client.get_session(conf),
interface=conf.service_credentials.interface,
region_name=conf.service_credentials.region_name)
@staticmethod
def _sanitize(rule, statistics):
"""Return the datapoints that correspond to the alarm granularity"""
# TODO(sileht): if there's no direct match, but there is an archive
# policy with granularity that's an even divisor or the period,
# we could potentially do a mean-of-means (or max-of-maxes or whatever,
# but not a stddev-of-stddevs).
# TODO(sileht): support alarm['exclude_outliers']
LOG.debug('sanitize stats %s', statistics)
statistics = [stats[VALUE] for stats in statistics
if stats[GRANULARITY] == rule['granularity']]
statistics = statistics[-rule['evaluation_periods']:]
LOG.debug('pruned statistics to %d', len(statistics))
return statistics
[docs]class GnocchiResourceThresholdEvaluator(GnocchiBase):
def _statistics(self, rule, start, end):
try:
return self._gnocchi_client.metric.get_measures(
metric=rule['metric'],
granularity=rule['granularity'],
start=start, stop=end,
resource_id=rule['resource_id'],
aggregation=rule['aggregation_method'])
except Exception as e:
LOG.warning(_LW('alarm stats retrieval failed: %s'),
e)
return []
[docs]class GnocchiAggregationMetricsThresholdEvaluator(GnocchiBase):
def _statistics(self, rule, start, end):
try:
# FIXME(sileht): In case of a heat autoscaling stack decide to
# delete an instance, the gnocchi metrics associated to this
# instance will be no more updated and when the alarm will ask
# for the aggregation, gnocchi will raise a 'No overlap'
# exception.
# So temporary set 'needed_overlap' to 0 to disable the
# gnocchi checks about missing points. For more detail see:
# https://bugs.launchpad.net/gnocchi/+bug/1479429
return self._gnocchi_client.metric.aggregation(
metrics=rule['metrics'],
granularity=rule['granularity'],
start=start, stop=end,
aggregation=rule['aggregation_method'],
needed_overlap=0)
except Exception as e:
LOG.warning(_LW('alarm stats retrieval failed: %s'), e)
return []
[docs]class GnocchiAggregationResourcesThresholdEvaluator(GnocchiBase):
def _statistics(self, rule, start, end):
# FIXME(sileht): In case of a heat autoscaling stack decide to
# delete an instance, the gnocchi metrics associated to this
# instance will be no more updated and when the alarm will ask
# for the aggregation, gnocchi will raise a 'No overlap'
# exception.
# So temporary set 'needed_overlap' to 0 to disable the
# gnocchi checks about missing points. For more detail see:
# https://bugs.launchpad.net/gnocchi/+bug/1479429
try:
return self._gnocchi_client.metric.aggregation(
metrics=rule['metric'],
granularity=rule['granularity'],
query=jsonutils.loads(rule['query']),
resource_type=rule["resource_type"],
start=start, stop=end,
aggregation=rule['aggregation_method'],
needed_overlap=0,
)
except Exception as e:
LOG.warning(_LW('alarm stats retrieval failed: %s'), e)
return []