Source code for aodh.evaluator.gnocchi

#
# 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'], 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'], 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'], 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 []

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