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fuel-plugin-openstack-telemetry
Code Issues Proposed changes

Aodh evaluator fix 

Add aggregation function into ceilometer statistic request from
aodh_evaluator.

Change-Id: I60caae282c71271db2776dd8cd741e480681e156
This commit is contained in:
Ildar Svetlov 2016-12-08 18:25:37 +04:00 committed by Ilya Tyaptin
parent a8f15cdc6d
commit 078ee65a9d
2 changed files with 249 additions and 1 deletions
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27
deployment_scripts/puppet/manifests/configure.pp
View File

@ -114,7 +114,7 @@ else {
}
ceilometer_config { 'notification/workers': value => max($::processorcount/3,1) }
# Workaround for fixing Ceilometer bug in MOS9.x
# Workaround for fixing Ceilometer and Aodh bugs in MOS9.x
file { '/usr/lib/python2.7/dist-packages/ceilometer/event/storage/impl_elasticsearch.py':
ensure => 'present',
content => file( 'telemetry/ceilometer_fixes/impl_elasticsearch.py' ),
@ -171,6 +171,21 @@ file { '/usr/lib/python2.7/dist-packages/ceilometer/storage/metrics/units.py':
notify => Service['ceilometer-service','ceilometer-agent-notification'],
}
file { '/usr/lib/python2.7/dist-packages/aodh/evaluator/threshold.py':
ensure => 'present',
content => file( 'telemetry/ceilometer_fixes/threshold.py' ),
mode => '0644',
owner => 'root',
group => 'root',
notify => Service['aodh-evaluator'],
require => File['threshold.pyc'],
}
file {'/usr/lib/python2.7/dist-packages/aodh/evaluator/threshold.pyc':
ensure => 'absent',
alias => 'threshold.pyc',
}
service {'ceilometer-agent-notification':
ensure => $service_ensure,
name => $::ceilometer::params::agent_notification_service_name,
@ -189,6 +204,16 @@ service { 'ceilometer-service':
tag => 'ceilometer-service',
}
service { 'aodh-evaluator':
ensure => $service_ensure,
name => $::aodh::params::evaluator_service_name,
enable => $enabled,
hasstatus => true,
hasrestart => true,
tag => 'aodh-evaluator',
provider => 'pacemaker',
}
Ceilometer_config<||> ~> Service['ceilometer-service']
class { 'telemetry':

223
deployment_scripts/puppet/modules/telemetry/files/ceilometer_fixes/threshold.py Normal file
View File

@ -0,0 +1,223 @@
#
# 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
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,
}
class ThresholdEvaluator(evaluator.Evaluator):
# the sliding evaluation window is extended to allow
# for reporting/ingestion lag
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
@classmethod
def _bound_duration(cls, 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 = (cls.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))
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'])
aggregates = [{'func': rule['statistic']}]
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'], aggregates=aggregates)
except Exception:
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
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:
return evaluator.UNKNOWN, None, statistics, len(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
else:
trending_state = evaluator.ALARM if compared[-1] else evaluator.OK
return None, trending_state, statistics, number_outside
def _transition_alarm(self, alarm, state, trending_state, statistics,
outside_count):
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(_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, state, 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)
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
state, trending_state, statistics, outside_count = self.evaluate_rule(
alarm.rule)
self._transition_alarm(alarm, state, trending_state, statistics,
outside_count)