204 lines
7.9 KiB
Python
204 lines
7.9 KiB
Python
#
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# Copyright 2013 Red Hat, Inc
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#
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# Author: Eoghan Glynn <eglynn@redhat.com>
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# Author: Mehdi Abaakouk <mehdi.abaakouk@enovance.com>
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import datetime
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import operator
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from oslo.utils import timeutils
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from ceilometer.alarm import evaluator
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from ceilometer.alarm.evaluator import utils
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from ceilometer.openstack.common.gettextutils import _
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from ceilometer.openstack.common import log
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LOG = log.getLogger(__name__)
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COMPARATORS = {
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'gt': operator.gt,
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'lt': operator.lt,
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'ge': operator.ge,
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'le': operator.le,
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'eq': operator.eq,
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'ne': operator.ne,
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}
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class ThresholdEvaluator(evaluator.Evaluator):
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# the sliding evaluation window is extended to allow
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# for reporting/ingestion lag
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look_back = 1
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# minimum number of datapoints within sliding window to
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# avoid unknown state
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quorum = 1
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@classmethod
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def _bound_duration(cls, alarm, constraints):
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"""Bound the duration of the statistics query."""
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now = timeutils.utcnow()
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# when exclusion of weak datapoints is enabled, we extend
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# the look-back period so as to allow a clearer sample count
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# trend to be established
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look_back = (cls.look_back if not alarm.rule.get('exclude_outliers')
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else alarm.rule['evaluation_periods'])
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window = (alarm.rule['period'] *
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(alarm.rule['evaluation_periods'] + look_back))
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start = now - datetime.timedelta(seconds=window)
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LOG.debug(_('query stats from %(start)s to '
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'%(now)s') % {'start': start, 'now': now})
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after = dict(field='timestamp', op='ge', value=start.isoformat())
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before = dict(field='timestamp', op='le', value=now.isoformat())
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constraints.extend([before, after])
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return constraints
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@staticmethod
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def _sanitize(alarm, statistics):
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"""Sanitize statistics."""
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LOG.debug(_('sanitize stats %s') % statistics)
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if alarm.rule.get('exclude_outliers'):
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key = operator.attrgetter('count')
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mean = utils.mean(statistics, key)
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stddev = utils.stddev(statistics, key, mean)
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lower = mean - 2 * stddev
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upper = mean + 2 * stddev
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inliers, outliers = utils.anomalies(statistics, key, lower, upper)
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if outliers:
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LOG.debug(_('excluded weak datapoints with sample counts %s'),
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[s.count for s in outliers])
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statistics = inliers
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else:
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LOG.debug('no excluded weak datapoints')
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# in practice statistics are always sorted by period start, not
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# strictly required by the API though
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statistics = statistics[-alarm.rule['evaluation_periods']:]
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LOG.debug(_('pruned statistics to %d') % len(statistics))
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return statistics
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def _statistics(self, alarm, query):
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"""Retrieve statistics over the current window."""
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LOG.debug(_('stats query %s') % query)
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try:
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return self._client.statistics.list(
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meter_name=alarm.rule['meter_name'], q=query,
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period=alarm.rule['period'])
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except Exception:
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LOG.exception(_('alarm stats retrieval failed'))
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return []
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def _sufficient(self, alarm, statistics):
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"""Check for the sufficiency of the data for evaluation.
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Ensure there is sufficient data for evaluation, transitioning to
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unknown otherwise.
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"""
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sufficient = len(statistics) >= self.quorum
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if not sufficient and alarm.state != evaluator.UNKNOWN:
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reason = _('%d datapoints are unknown') % alarm.rule[
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'evaluation_periods']
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reason_data = self._reason_data('unknown',
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alarm.rule['evaluation_periods'],
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None)
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self._refresh(alarm, evaluator.UNKNOWN, reason, reason_data)
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return sufficient
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@staticmethod
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def _reason_data(disposition, count, most_recent):
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"""Create a reason data dictionary for this evaluator type."""
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return {'type': 'threshold', 'disposition': disposition,
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'count': count, 'most_recent': most_recent}
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@classmethod
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def _reason(cls, alarm, statistics, distilled, state):
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"""Fabricate reason string."""
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count = len(statistics)
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disposition = 'inside' if state == evaluator.OK else 'outside'
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last = getattr(statistics[-1], alarm.rule['statistic'])
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transition = alarm.state != state
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reason_data = cls._reason_data(disposition, count, last)
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if transition:
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return (_('Transition to %(state)s due to %(count)d samples'
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' %(disposition)s threshold, most recent:'
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' %(most_recent)s')
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% dict(reason_data, state=state)), reason_data
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return (_('Remaining as %(state)s due to %(count)d samples'
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' %(disposition)s threshold, most recent: %(most_recent)s')
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% dict(reason_data, state=state)), reason_data
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def _transition(self, alarm, statistics, compared):
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"""Transition alarm state if necessary.
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The transition rules are currently hardcoded as:
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- transitioning from a known state requires an unequivocal
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set of datapoints
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- transitioning from unknown is on the basis of the most
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recent datapoint if equivocal
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Ultimately this will be policy-driven.
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"""
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distilled = all(compared)
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unequivocal = distilled or not any(compared)
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unknown = alarm.state == evaluator.UNKNOWN
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continuous = alarm.repeat_actions
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if unequivocal:
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state = evaluator.ALARM if distilled else evaluator.OK
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reason, reason_data = self._reason(alarm, statistics,
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distilled, state)
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if alarm.state != state or continuous:
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self._refresh(alarm, state, reason, reason_data)
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elif unknown or continuous:
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trending_state = evaluator.ALARM if compared[-1] else evaluator.OK
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state = trending_state if unknown else alarm.state
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reason, reason_data = self._reason(alarm, statistics,
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distilled, state)
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self._refresh(alarm, state, reason, reason_data)
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def evaluate(self, alarm):
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if not self.within_time_constraint(alarm):
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LOG.debug(_('Attempted to evaluate alarm %s, but it is not '
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'within its time constraint.') % alarm.alarm_id)
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return
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query = self._bound_duration(
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alarm,
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alarm.rule['query']
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)
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statistics = self._sanitize(
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alarm,
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self._statistics(alarm, query)
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)
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if self._sufficient(alarm, statistics):
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def _compare(stat):
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op = COMPARATORS[alarm.rule['comparison_operator']]
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value = getattr(stat, alarm.rule['statistic'])
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limit = alarm.rule['threshold']
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LOG.debug(_('comparing value %(value)s against threshold'
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' %(limit)s') %
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{'value': value, 'limit': limit})
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return op(value, limit)
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self._transition(alarm,
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statistics,
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map(_compare, statistics))
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