# # Copyright 2013-2015 Red Hat, Inc # # 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. import copy import datetime import operator import six from ceilometerclient import client as ceiloclient from oslo_log import log from oslo_utils import timeutils from aodh import evaluator from aodh.evaluator import utils from aodh.i18n import _, _LW LOG = log.getLogger(__name__) COMPARATORS = { 'gt': operator.gt, 'lt': operator.lt, 'ge': operator.ge, 'le': operator.le, 'eq': operator.eq, 'ne': operator.ne, } class ThresholdEvaluator(evaluator.Evaluator): # the sliding evaluation window is extended to allow # for reporting/ingestion lag look_back = 1 def __init__(self, conf, notifier): super(ThresholdEvaluator, self).__init__(conf, notifier) auth_config = conf.service_credentials self._client = ceiloclient.get_client( 2, os_auth_url=auth_config.os_auth_url, os_region_name=auth_config.os_region_name, os_tenant_name=auth_config.os_tenant_name, os_password=auth_config.os_password, os_username=auth_config.os_username, os_cacert=auth_config.os_cacert, os_endpoint_type=auth_config.os_endpoint_type, insecure=auth_config.insecure, timeout=conf.http_timeout, os_user_domain_id=auth_config.os_user_domain_id, os_project_name=auth_config.os_project_name, os_project_domain_id=auth_config.os_project_domain_id, ) @classmethod def _bound_duration(cls, alarm): """Bound the duration of the statistics query.""" now = timeutils.utcnow() # when exclusion of weak datapoints is enabled, we extend # the look-back period so as to allow a clearer sample count # trend to be established look_back = (cls.look_back if not alarm.rule.get('exclude_outliers') else alarm.rule['evaluation_periods']) window = ((alarm.rule.get('period', None) or alarm.rule['granularity']) * (alarm.rule['evaluation_periods'] + look_back)) start = now - datetime.timedelta(seconds=window) LOG.debug(_('query stats from %(start)s to ' '%(now)s') % {'start': start, 'now': now}) return start.isoformat(), now.isoformat() @staticmethod def _sanitize(alarm, statistics): """Sanitize statistics.""" LOG.debug(_('sanitize stats %s') % statistics) if alarm.rule.get('exclude_outliers'): key = operator.attrgetter('count') mean = utils.mean(statistics, key) stddev = utils.stddev(statistics, key, mean) lower = mean - 2 * stddev upper = mean + 2 * stddev inliers, outliers = utils.anomalies(statistics, key, lower, upper) if outliers: LOG.debug(_('excluded weak datapoints with sample counts %s'), [s.count for s in outliers]) statistics = inliers else: LOG.debug('no excluded weak datapoints') # in practice statistics are always sorted by period start, not # strictly required by the API though statistics = statistics[-alarm.rule['evaluation_periods']:] result_statistics = [getattr(stat, alarm.rule['statistic']) for stat in statistics] LOG.debug(_('pruned statistics to %d') % len(statistics)) return result_statistics def _statistics(self, alarm, start, end): """Retrieve statistics over the current window.""" after = dict(field='timestamp', op='ge', value=start) before = dict(field='timestamp', op='le', value=end) query = copy.copy(alarm.rule['query']) query.extend([before, after]) LOG.debug(_('stats query %s') % query) try: return self._client.statistics.list( meter_name=alarm.rule['meter_name'], q=query, period=alarm.rule['period']) except Exception: LOG.exception(_('alarm stats retrieval failed')) return [] def _sufficient(self, alarm, statistics): """Check for the sufficiency of the data for evaluation. Ensure there is sufficient data for evaluation, transitioning to unknown otherwise. """ sufficient = len(statistics) >= alarm.rule['evaluation_periods'] if not sufficient and alarm.state != evaluator.UNKNOWN: LOG.warn(_LW('Expecting %(expected)d datapoints but only get ' '%(actual)d') % { 'expected': alarm.rule['evaluation_periods'], 'actual': len(statistics)}) # Reason is not same as log message because we want to keep # consistent since thirdparty software may depend on old format. reason = _('%d datapoints are unknown') % alarm.rule[ 'evaluation_periods'] last = None if not statistics else statistics[-1] reason_data = self._reason_data('unknown', alarm.rule['evaluation_periods'], last) self._refresh(alarm, evaluator.UNKNOWN, reason, reason_data) return sufficient @staticmethod def _reason_data(disposition, count, most_recent): """Create a reason data dictionary for this evaluator type.""" return {'type': 'threshold', 'disposition': disposition, 'count': count, 'most_recent': most_recent} @classmethod def _reason(cls, alarm, statistics, distilled, state): """Fabricate reason string.""" count = len(statistics) disposition = 'inside' if state == evaluator.OK else 'outside' last = statistics[-1] transition = alarm.state != state reason_data = cls._reason_data(disposition, count, last) if transition: return (_('Transition to %(state)s due to %(count)d samples' ' %(disposition)s threshold, most recent:' ' %(most_recent)s') % dict(reason_data, state=state)), reason_data return (_('Remaining as %(state)s due to %(count)d samples' ' %(disposition)s threshold, most recent: %(most_recent)s') % dict(reason_data, state=state)), reason_data def _transition(self, alarm, statistics, compared): """Transition alarm state if necessary. The transition rules are currently hardcoded as: - transitioning from a known state requires an unequivocal set of datapoints - transitioning from unknown is on the basis of the most recent datapoint if equivocal Ultimately this will be policy-driven. """ distilled = all(compared) unequivocal = distilled or not any(compared) unknown = alarm.state == evaluator.UNKNOWN continuous = alarm.repeat_actions if unequivocal: state = evaluator.ALARM if distilled else evaluator.OK reason, reason_data = self._reason(alarm, statistics, distilled, state) if alarm.state != state or continuous: self._refresh(alarm, state, reason, reason_data) elif unknown or continuous: trending_state = evaluator.ALARM if compared[-1] else evaluator.OK state = trending_state if unknown else alarm.state reason, reason_data = self._reason(alarm, statistics, distilled, state) self._refresh(alarm, state, reason, reason_data) def evaluate(self, alarm): if not self.within_time_constraint(alarm): LOG.debug(_('Attempted to evaluate alarm %s, but it is not ' 'within its time constraint.') % alarm.alarm_id) return start, end = self._bound_duration(alarm) statistics = self._statistics(alarm, start, end) statistics = self._sanitize(alarm, statistics) if self._sufficient(alarm, statistics): def _compare(value): op = COMPARATORS[alarm.rule['comparison_operator']] limit = alarm.rule['threshold'] LOG.debug(_('comparing value %(value)s against threshold' ' %(limit)s') % {'value': value, 'limit': limit}) return op(value, limit) self._transition(alarm, statistics, list(six.moves.map(_compare, statistics)))