17feef6c66
ujson is faster than jsonutils, and we do not need any fancy feature jsonutils might offer. This also has the benefit of removing a big dependency on Aodh. Change-Id: I4773ac55a5aedf4960c4ce611873813922ca01e5
164 lines
7.3 KiB
Python
164 lines
7.3 KiB
Python
#
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# Copyright 2015 eNovance
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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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from gnocchiclient import client
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from gnocchiclient import exceptions
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from oslo_log import log
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from aodh.evaluator import threshold
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from aodh import keystone_client
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import ujson
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LOG = log.getLogger(__name__)
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# The list of points that Gnocchi API returned is composed
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# of tuples with (timestamp, granularity, value)
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GRANULARITY = 1
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VALUE = 2
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class GnocchiBase(threshold.ThresholdEvaluator):
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def __init__(self, conf):
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super(GnocchiBase, self).__init__(conf)
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self._gnocchi_client = client.Client(
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'1', keystone_client.get_session(conf),
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adapter_options={
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'interface': conf.service_credentials.interface,
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'region_name': conf.service_credentials.region_name})
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@staticmethod
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def _sanitize(rule, statistics):
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"""Return the datapoints that correspond to the alarm granularity"""
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# TODO(sileht): if there's no direct match, but there is an archive
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# policy with granularity that's an even divisor or the period,
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# we could potentially do a mean-of-means (or max-of-maxes or whatever,
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# but not a stddev-of-stddevs).
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# TODO(sileht): support alarm['exclude_outliers']
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LOG.debug('sanitize stats %s', statistics)
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statistics = [stats[VALUE] for stats in statistics
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if stats[GRANULARITY] == rule['granularity']]
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if not statistics:
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raise threshold.InsufficientDataError(
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"No datapoint for granularity %s" % rule['granularity'], [])
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statistics = statistics[-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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class GnocchiResourceThresholdEvaluator(GnocchiBase):
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def _statistics(self, rule, start, end):
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try:
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return self._gnocchi_client.metric.get_measures(
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metric=rule['metric'],
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granularity=rule['granularity'],
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start=start, stop=end,
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resource_id=rule['resource_id'],
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aggregation=rule['aggregation_method'])
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except exceptions.MetricNotFound:
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raise threshold.InsufficientDataError(
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'metric %s for resource %s does not exists' %
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(rule['metric'], rule['resource_id']), [])
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except exceptions.ResourceNotFound:
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raise threshold.InsufficientDataError(
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'resource %s does not exists' % rule['resource_id'], [])
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except exceptions.NotFound:
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# TODO(sileht): gnocchiclient should raise a explicit
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# exception for AggregationNotFound, this API endpoint
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# can only raise 3 different 404, so we are safe to
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# assume this is an AggregationNotFound for now.
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raise threshold.InsufficientDataError(
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'aggregation %s does not exist for '
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'metric %s of resource %s' % (rule['aggregation_method'],
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rule['metric'],
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rule['resource_id']),
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[])
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except Exception as e:
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msg = 'alarm statistics retrieval failed: %s' % e
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LOG.warning(msg)
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raise threshold.InsufficientDataError(msg, [])
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class GnocchiAggregationMetricsThresholdEvaluator(GnocchiBase):
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def _statistics(self, rule, start, end):
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try:
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# FIXME(sileht): In case of a heat autoscaling stack decide to
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# delete an instance, the gnocchi metrics associated to this
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# instance will be no more updated and when the alarm will ask
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# for the aggregation, gnocchi will raise a 'No overlap'
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# exception.
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# So temporary set 'needed_overlap' to 0 to disable the
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# gnocchi checks about missing points. For more detail see:
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# https://bugs.launchpad.net/gnocchi/+bug/1479429
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return self._gnocchi_client.metric.aggregation(
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metrics=rule['metrics'],
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granularity=rule['granularity'],
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start=start, stop=end,
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aggregation=rule['aggregation_method'],
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needed_overlap=0)
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except exceptions.MetricNotFound:
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raise threshold.InsufficientDataError(
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'At least of metrics in %s does not exist' %
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rule['metrics'], [])
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except exceptions.NotFound:
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# TODO(sileht): gnocchiclient should raise a explicit
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# exception for AggregationNotFound, this API endpoint
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# can only raise 3 different 404, so we are safe to
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# assume this is an AggregationNotFound for now.
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raise threshold.InsufficientDataError(
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'aggregation %s does not exist for at least one '
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'metrics in %s' % (rule['aggregation_method'],
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rule['metrics']), [])
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except Exception as e:
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msg = 'alarm statistics retrieval failed: %s' % e
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LOG.warning(msg)
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raise threshold.InsufficientDataError(msg, [])
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class GnocchiAggregationResourcesThresholdEvaluator(GnocchiBase):
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def _statistics(self, rule, start, end):
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# FIXME(sileht): In case of a heat autoscaling stack decide to
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# delete an instance, the gnocchi metrics associated to this
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# instance will be no more updated and when the alarm will ask
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# for the aggregation, gnocchi will raise a 'No overlap'
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# exception.
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# So temporary set 'needed_overlap' to 0 to disable the
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# gnocchi checks about missing points. For more detail see:
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# https://bugs.launchpad.net/gnocchi/+bug/1479429
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try:
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return self._gnocchi_client.metric.aggregation(
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metrics=rule['metric'],
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granularity=rule['granularity'],
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query=ujson.loads(rule['query']),
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resource_type=rule["resource_type"],
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start=start, stop=end,
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aggregation=rule['aggregation_method'],
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needed_overlap=0,
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)
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except exceptions.MetricNotFound:
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raise threshold.InsufficientDataError(
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'metric %s does not exists' % rule['metric'], [])
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except exceptions.NotFound:
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# TODO(sileht): gnocchiclient should raise a explicit
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# exception for AggregationNotFound, this API endpoint
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# can only raise 3 different 404, so we are safe to
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# assume this is an AggregationNotFound for now.
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raise threshold.InsufficientDataError(
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'aggregation %s does not exist for at least one '
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'metric of the query' % rule['aggregation_method'], [])
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except Exception as e:
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msg = 'alarm statistics retrieval failed: %s' % e
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LOG.warning(msg)
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raise threshold.InsufficientDataError(msg, [])
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