Aodh evaluator fix
Add aggregation function into ceilometer statistic request from aodh_evaluator. Change-Id: I60caae282c71271db2776dd8cd741e480681e156
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@ -114,7 +114,7 @@ else {
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}
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}
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ceilometer_config { 'notification/workers': value => max($::processorcount/3,1) }
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ceilometer_config { 'notification/workers': value => max($::processorcount/3,1) }
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# Workaround for fixing Ceilometer bug in MOS9.x
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# Workaround for fixing Ceilometer and Aodh bugs in MOS9.x
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file { '/usr/lib/python2.7/dist-packages/ceilometer/event/storage/impl_elasticsearch.py':
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file { '/usr/lib/python2.7/dist-packages/ceilometer/event/storage/impl_elasticsearch.py':
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ensure => 'present',
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ensure => 'present',
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content => file( 'telemetry/ceilometer_fixes/impl_elasticsearch.py' ),
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content => file( 'telemetry/ceilometer_fixes/impl_elasticsearch.py' ),
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@ -171,6 +171,21 @@ file { '/usr/lib/python2.7/dist-packages/ceilometer/storage/metrics/units.py':
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notify => Service['ceilometer-service','ceilometer-agent-notification'],
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notify => Service['ceilometer-service','ceilometer-agent-notification'],
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}
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}
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file { '/usr/lib/python2.7/dist-packages/aodh/evaluator/threshold.py':
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ensure => 'present',
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content => file( 'telemetry/ceilometer_fixes/threshold.py' ),
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mode => '0644',
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owner => 'root',
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group => 'root',
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notify => Service['aodh-evaluator'],
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require => File['threshold.pyc'],
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}
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file {'/usr/lib/python2.7/dist-packages/aodh/evaluator/threshold.pyc':
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ensure => 'absent',
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alias => 'threshold.pyc',
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}
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service {'ceilometer-agent-notification':
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service {'ceilometer-agent-notification':
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ensure => $service_ensure,
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ensure => $service_ensure,
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name => $::ceilometer::params::agent_notification_service_name,
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name => $::ceilometer::params::agent_notification_service_name,
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@ -189,6 +204,16 @@ service { 'ceilometer-service':
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tag => 'ceilometer-service',
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tag => 'ceilometer-service',
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}
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}
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service { 'aodh-evaluator':
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ensure => $service_ensure,
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name => $::aodh::params::evaluator_service_name,
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enable => $enabled,
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hasstatus => true,
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hasrestart => true,
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tag => 'aodh-evaluator',
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provider => 'pacemaker',
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}
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Ceilometer_config<||> ~> Service['ceilometer-service']
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Ceilometer_config<||> ~> Service['ceilometer-service']
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class { 'telemetry':
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class { 'telemetry':
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@ -0,0 +1,223 @@
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#
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# Copyright 2013-2015 Red Hat, Inc
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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 copy
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import datetime
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import operator
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import six
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from ceilometerclient import client as ceiloclient
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from oslo_log import log
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from oslo_utils import timeutils
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from aodh import evaluator
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from aodh.evaluator import utils
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from aodh.i18n import _, _LW
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from aodh import keystone_client
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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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def __init__(self, conf):
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super(ThresholdEvaluator, self).__init__(conf)
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self._cm_client = None
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@property
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def cm_client(self):
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if self._cm_client is None:
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auth_config = self.conf.service_credentials
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self._cm_client = ceiloclient.get_client(
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version=2,
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session=keystone_client.get_session(self.conf),
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# ceiloclient adapter options
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region_name=auth_config.region_name,
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interface=auth_config.interface,
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)
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return self._cm_client
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@classmethod
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def _bound_duration(cls, rule):
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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 rule.get('exclude_outliers')
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else rule['evaluation_periods'])
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window = ((rule.get('period', None) or rule['granularity'])
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* (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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return start.isoformat(), now.isoformat()
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@staticmethod
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def _sanitize(rule, statistics):
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"""Sanitize statistics."""
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LOG.debug('sanitize stats %s', statistics)
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if 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[-rule['evaluation_periods']:]
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result_statistics = [getattr(stat, rule['statistic'])
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for stat in statistics]
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LOG.debug('pruned statistics to %d', len(statistics))
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return result_statistics
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def _statistics(self, rule, start, end):
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"""Retrieve statistics over the current window."""
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after = dict(field='timestamp', op='ge', value=start)
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before = dict(field='timestamp', op='le', value=end)
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query = copy.copy(rule['query'])
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aggregates = [{'func': rule['statistic']}]
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query.extend([before, after])
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LOG.debug('stats query %s', query)
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try:
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return self.cm_client.statistics.list(
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meter_name=rule['meter_name'], q=query,
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period=rule['period'], aggregates=aggregates)
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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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@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, state, count):
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"""Fabricate reason string."""
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if state == evaluator.OK:
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disposition = 'inside'
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count = len(statistics) - count
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else:
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disposition = 'outside'
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last = statistics[-1] if statistics else None
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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 evaluate_rule(self, alarm_rule):
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"""Evaluate alarm rule.
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:returns: state, trending state and statistics.
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"""
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start, end = self._bound_duration(alarm_rule)
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statistics = self._statistics(alarm_rule, start, end)
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statistics = self._sanitize(alarm_rule, statistics)
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sufficient = len(statistics) >= alarm_rule['evaluation_periods']
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if not sufficient:
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return evaluator.UNKNOWN, None, statistics, len(statistics)
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def _compare(value):
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op = COMPARATORS[alarm_rule['comparison_operator']]
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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', {'value': value, 'limit': limit})
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return op(value, limit)
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compared = list(six.moves.map(_compare, statistics))
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distilled = all(compared)
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unequivocal = distilled or not any(compared)
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number_outside = len([c for c in compared if c])
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if unequivocal:
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state = evaluator.ALARM if distilled else evaluator.OK
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return state, None, statistics, number_outside
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else:
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trending_state = evaluator.ALARM if compared[-1] else evaluator.OK
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return None, trending_state, statistics, number_outside
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def _transition_alarm(self, alarm, state, trending_state, statistics,
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outside_count):
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unknown = alarm.state == evaluator.UNKNOWN
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continuous = alarm.repeat_actions
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if trending_state:
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if unknown or continuous:
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state = trending_state if unknown else alarm.state
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reason, reason_data = self._reason(alarm, statistics, state,
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outside_count)
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self._refresh(alarm, state, reason, reason_data)
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return
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if state == evaluator.UNKNOWN and not unknown:
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LOG.warning(_LW('Expecting %(expected)d datapoints but only get '
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'%(actual)d') % {
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'expected': alarm.rule['evaluation_periods'],
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'actual': len(statistics)})
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# Reason is not same as log message because we want to keep
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# consistent since thirdparty software may depend on old format.
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reason = _('%d datapoints are unknown') % alarm.rule[
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'evaluation_periods']
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last = None if not statistics else statistics[-1]
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reason_data = self._reason_data('unknown',
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alarm.rule['evaluation_periods'],
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last)
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self._refresh(alarm, state, reason, reason_data)
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elif state and (alarm.state != state or continuous):
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reason, reason_data = self._reason(alarm, statistics, state,
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outside_count)
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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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state, trending_state, statistics, outside_count = self.evaluate_rule(
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alarm.rule)
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self._transition_alarm(alarm, state, trending_state, statistics,
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outside_count)
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