def _statistics(self, rule, start, end):
try:
# FIXME(sileht): In case of a heat autoscaling stack decide to
# delete an instance, the gnocchi metrics associated to this
# instance will be no more updated and when the alarm will ask
# for the aggregation, gnocchi will raise a 'No overlap'
# exception.
# So temporary set 'needed_overlap' to 0 to disable the
# gnocchi checks about missing points. For more detail see:
# https://bugs.launchpad.net/gnocchi/+bug/1479429
return self._gnocchi_client.metric.aggregation(
metrics=rule['metrics'],
start=start,
stop=end,
aggregation=rule['aggregation_method'],
needed_overlap=0)
except exceptions.MetricNotFound:
raise threshold.InsufficientDataError(
'At least of metrics in %s does not exist' % rule['metrics'],
[])
except exceptions.NotFound:
# TODO(sileht): gnocchiclient should raise a explicit
# exception for AggregationNotFound, this API endpoint
# can only raise 3 different 404, so we are safe to
# assume this is an AggregationNotFound for now.
raise threshold.InsufficientDataError(
'aggregation %s does not exist for at least one '
'metrics in %s' %
(rule['aggregation_method'], rule['metrics']), [])
except Exception as e:
msg = 'alarm statistics retrieval failed: %s' % e
LOG.warning(msg)
raise threshold.InsufficientDataError(msg, [])
def _statistics(self, rule, start, end):
try:
return self._gnocchi_client.metric.get_measures(
metric=rule['metric'],
start=start,
stop=end,
resource_id=rule['resource_id'],
aggregation=rule['aggregation_method'])
except exceptions.MetricNotFound:
raise threshold.InsufficientDataError(
'metric %s for resource %s does not exists' %
(rule['metric'], rule['resource_id']), [])
except exceptions.ResourceNotFound:
raise threshold.InsufficientDataError(
'resource %s does not exists' % rule['resource_id'], [])
except exceptions.NotFound:
# TODO(sileht): gnocchiclient should raise a explicit
# exception for AggregationNotFound, this API endpoint
# can only raise 3 different 404, so we are safe to
# assume this is an AggregationNotFound for now.
raise threshold.InsufficientDataError(
'aggregation %s does not exist for '
'metric %s of resource %s' %
(rule['aggregation_method'], rule['metric'],
rule['resource_id']), [])
except Exception as e:
msg = 'alarm statistics retrieval failed: %s' % e
LOG.warning(msg)
raise threshold.InsufficientDataError(msg, [])
def _get_unhealthy_members(self, pool_id):
"""Get number of unhealthy members in a pool.
The member(virutual machine) operating_status keeps ERROR after
creation before the application is up and running inside, it should be
ignored during the check.
"""
unhealthy_members = []
try:
ret = self.lb_client.member_list(pool_id)
except Exception as e:
LOG.warning("Failed to communicate with load balancing service, "
"error: %s", six.text_type(e))
raise threshold.InsufficientDataError(
'failed to communicate with load balancing service',
[]
)
if getattr(ret, 'status_code', None):
# Some error happened
raise threshold.InsufficientDataError(ret.content, [])
for m in ret.get("members", []):
try:
created_time = parser.parse(m['created_at'], ignoretz=True)
except ValueError:
LOG.warning('Failed to parse the member created time.')
continue
now = timeutils.utcnow()
t = self.conf.member_creation_time
if now - created_time < datetime.timedelta(seconds=t):
LOG.debug("Ignore member which was created within %ss", t)
continue
if m["admin_state_up"] and m["operating_status"] == "ERROR":
unhealthy_members.append(m)
return unhealthy_members
def _sanitize(rule, statistics):
"""Return the datapoints that correspond to the alarm granularity"""
# TODO(sileht): if there's no direct match, but there is an archive
# policy with granularity that's an even divisor or the period,
# we could potentially do a mean-of-means (or max-of-maxes or whatever,
# but not a stddev-of-stddevs).
# TODO(sileht): support alarm['exclude_outliers']
LOG.debug('sanitize stats %s', statistics)
statistics = [
stats[VALUE] for stats in statistics
if stats[GRANULARITY] == rule['granularity']
]
if not statistics:
raise threshold.InsufficientDataError(
"No datapoint for granularity %s" % rule['granularity'], [])
statistics = statistics[-rule['evaluation_periods']:]
LOG.debug('pruned statistics to %d', len(statistics))
return statistics
