aodh.evaluator.threshold

Source code for aodh.evaluator.threshold

#
# 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 ceilometerclient import exc as ceiloexc
from oslo_config import cfg
from oslo_log import log
from oslo_utils import timeutils

from aodh import evaluator
from aodh.evaluator import utils
from aodh.i18n import _
from aodh import keystone_client

LOG = log.getLogger(__name__)

COMPARATORS = {
    'gt': operator.gt,
    'lt': operator.lt,
    'ge': operator.ge,
    'le': operator.le,
    'eq': operator.eq,
    'ne': operator.ne,
}

OPTS = [
    cfg.IntOpt('additional_ingestion_lag',
               min=0,
               default=0,
               help='The number of seconds to extend the evaluation windows '
               'to compensate the reporting/ingestion lag.')
]


[docs]class InsufficientDataError(Exception): def __init__(self, reason, statistics): self.reason = reason self.statistics = statistics super(InsufficientDataError, self).__init__(reason)
[docs]class ThresholdEvaluator(evaluator.Evaluator): # the sliding evaluation window is extended to allow # the reporting/ingestion lag this can be increased # with 'additional_ingestion_lag' seconds if needed. look_back = 1 def __init__(self, conf): super(ThresholdEvaluator, self).__init__(conf) self._cm_client = None @property def cm_client(self): if self._cm_client is None: auth_config = self.conf.service_credentials self._cm_client = ceiloclient.get_client( version=2, session=keystone_client.get_session(self.conf), # ceiloclient adapter options region_name=auth_config.region_name, interface=auth_config.interface, ) return self._cm_client def _bound_duration(self, rule): """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 = (self.look_back if not rule.get('exclude_outliers') else rule['evaluation_periods']) window = ((rule.get('period', None) or rule['granularity']) * (rule['evaluation_periods'] + look_back) + self.conf.additional_ingestion_lag) 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(rule, statistics): """Sanitize statistics.""" LOG.debug('sanitize stats %s', statistics) if 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[-rule['evaluation_periods']:] result_statistics = [getattr(stat, rule['statistic']) for stat in statistics] LOG.debug('pruned statistics to %d', len(statistics)) return result_statistics def _statistics(self, rule, 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(rule['query']) query.extend([before, after]) LOG.debug('stats query %s', query) try: return self.cm_client.statistics.list( meter_name=rule['meter_name'], q=query, period=rule['period']) except Exception as e: if isinstance(e, ceiloexc.HTTPException) and e.code == 410: LOG.warning("This telemetry installation is not configured to " "support alarm of type 'threshold', they should " "be disabled or removed.") else: LOG.exception(_('alarm stats retrieval failed')) return [] @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, state, count): """Fabricate reason string.""" if state == evaluator.OK: disposition = 'inside' count = len(statistics) - count else: disposition = 'outside' last = statistics[-1] if statistics else None 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)
[docs] def evaluate_rule(self, alarm_rule): """Evaluate alarm rule. :returns: state, trending state and statistics. """ start, end = self._bound_duration(alarm_rule) statistics = self._statistics(alarm_rule, start, end) statistics = self._sanitize(alarm_rule, statistics) sufficient = len(statistics) >= alarm_rule['evaluation_periods'] if not sufficient: raise InsufficientDataError( '%d datapoints are unknown' % alarm_rule['evaluation_periods'], 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) compared = list(six.moves.map(_compare, statistics)) distilled = all(compared) unequivocal = distilled or not any(compared) number_outside = len([c for c in compared if c]) if unequivocal: state = evaluator.ALARM if distilled else evaluator.OK return state, None, statistics, number_outside, None else: trending_state = evaluator.ALARM if compared[-1] else evaluator.OK return None, trending_state, statistics, number_outside, None
def _transition_alarm(self, alarm, state, trending_state, statistics, outside_count, unknown_reason): unknown = alarm.state == evaluator.UNKNOWN continuous = alarm.repeat_actions if trending_state: if unknown or continuous: state = trending_state if unknown else alarm.state reason, reason_data = self._reason(alarm, statistics, state, outside_count) self._refresh(alarm, state, reason, reason_data) return if state == evaluator.UNKNOWN and not unknown: LOG.warning('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. last = None if not statistics else statistics[-1] reason_data = self._reason_data('unknown', alarm.rule['evaluation_periods'], last) self._refresh(alarm, state, unknown_reason, reason_data) elif state and (alarm.state != state or continuous): reason, reason_data = self._reason(alarm, statistics, state, outside_count) self._refresh(alarm, state, reason, reason_data)
[docs] 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 try: evaluation = self.evaluate_rule(alarm.rule) except InsufficientDataError as e: evaluation = (evaluator.UNKNOWN, None, e.statistics, 0, e.reason) self._transition_alarm(alarm, *evaluation)
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