Fixing performance for ceilometer statistics list and use

monasca's group_by option to avoid metrics-list calll which is
not necessary.

Change-Id: Id65549088d15141daccc78ef90e735c550da63c8
This commit is contained in:
Atul Aggarwal 2016-08-30 16:46:27 -07:00
parent 02ca4e80fe
commit ac78ecbb02
2 changed files with 75 additions and 99 deletions

View File

@ -16,6 +16,7 @@
"""Simple monasca storage backend.
"""
from collections import defaultdict
import datetime
import operator
@ -511,35 +512,26 @@ class Connection(base.Connection):
period = period if period \
else cfg.CONF.monasca.default_stats_period
_search_args = dict(
name=filter.meter,
dimensions=dims_filter,
start_time=filter.start_timestamp,
end_time=filter.end_timestamp,
period=period,
statistics=','.join(statistics),
)
_search_args = {k: v for k, v in _search_args.items()
if v is not None}
if groupby:
_metric_args = dict(name=filter.meter,
dimensions=dims_filter)
group_stats_list = []
_search_args['group_by'] = '*'
stats_list = self.mc.statistics_list(**_search_args)
group_stats_dict = defaultdict(list)
for metric in self.mc.metrics_list(**_metric_args):
_search_args = dict(
name=metric['name'],
dimensions=metric['dimensions'],
start_time=filter.start_timestamp,
end_time=filter.end_timestamp,
period=period,
statistics=','.join(statistics),
merge_metrics=False)
_search_args = {k: v for k, v in _search_args.items()
if v is not None}
stats_list = self.mc.statistics_list(**_search_args)
group_stats_list.extend(stats_list)
group_stats_dict = {}
for stats in group_stats_list:
for stats in stats_list:
groupby_val = stats['dimensions'].get(groupby)
stats_list = group_stats_dict.get(groupby_val)
if stats_list:
stats_list.append(stats)
else:
group_stats_dict[groupby_val] = [stats]
group_stats_dict[groupby_val].append(stats)
def get_max(items):
return max(items)
@ -583,9 +575,8 @@ class Connection(base.Connection):
count_list.append(stats_dict['count'])
ts_list.append(stats_dict['timestamp'])
group_statistics['unit'] = (stats['dimensions'].
get('unit'))
group_statistics['unit'] = (stats['dimensions'].
get('unit'))
if len(max_list):
group_statistics['max'] = get_max(max_list)
@ -598,51 +589,47 @@ class Connection(base.Connection):
if len(count_list):
group_statistics['count'] = get_count(count_list)
group_statistics['end_timestamp'] = get_max(ts_list)
group_statistics['timestamp'] = get_min(ts_list)
ts_start = timeutils.parse_isotime(
group_statistics['timestamp']).replace(tzinfo=None)
ts_end = timeutils.parse_isotime(
group_statistics['end_timestamp']).replace(tzinfo=None)
del group_statistics['end_timestamp']
if 'count' in group_statistics:
group_statistics['count'] = int(group_statistics['count'])
unit = group_statistics['unit']
del group_statistics['unit']
if aggregate:
group_statistics['aggregate'] = {}
for a in aggregate:
key = '%s%s' % (a.func, '/%s' % a.param if a.param
else '')
group_statistics['aggregate'][key] = (
group_statistics.get(key))
yield api_models.Statistics(
unit=unit,
period=period,
period_start=ts_start,
period_end=ts_end,
duration=period,
duration_start=ts_start,
duration_end=ts_end,
groupby={groupby: group_key},
**group_statistics
)
else:
_search_args = dict(
name=filter.meter,
dimensions=dims_filter,
start_time=filter.start_timestamp,
end_time=filter.end_timestamp,
period=period,
statistics=','.join(statistics),
merge_metrics=True)
_search_args = {k: v for k, v in _search_args.items()
if v is not None}
unit = group_statistics.get('unit')
if 'unit' in group_statistics:
del group_statistics['unit']
if aggregate:
group_statistics['aggregate'] = {}
for a in aggregate:
key = '%s%s' % (a.func, '/%s' % a.param if a.param
else '')
group_statistics['aggregate'][key] = (
group_statistics.get(key))
if group_statistics and len(ts_list):
group_statistics['end_timestamp'] = get_max(ts_list)
group_statistics['timestamp'] = get_min(ts_list)
ts_start = timeutils.parse_isotime(
group_statistics['timestamp']).replace(tzinfo=None)
ts_end = timeutils.parse_isotime(
group_statistics['end_timestamp']).replace(tzinfo=None)
del group_statistics['end_timestamp']
if ts_start and ts_end:
yield api_models.Statistics(
unit=unit,
period=period,
period_start=ts_start,
period_end=ts_end,
duration=period,
duration_start=ts_start,
duration_end=ts_end,
groupby={groupby: group_key},
**group_statistics
)
else:
_search_args['merge_metrics'] = True
stats_list = self.mc.statistics_list(**_search_args)
for stats in stats_list:
for s in stats['statistics']:
@ -661,18 +648,19 @@ class Connection(base.Connection):
key = '%s%s' % (a.func, '/%s' % a.param if a.param
else '')
stats_dict['aggregate'][key] = stats_dict.get(key)
yield api_models.Statistics(
unit=stats['dimensions'].get('unit'),
period=period,
period_start=ts_start,
period_end=ts_end,
duration=period,
duration_start=ts_start,
duration_end=ts_end,
groupby={u'': u''},
**stats_dict
)
unit = stats['dimensions'].get('unit')
if ts_start and ts_end:
yield api_models.Statistics(
unit=unit,
period=period,
period_start=ts_start,
period_end=ts_end,
duration=period,
duration_start=ts_start,
duration_end=ts_end,
groupby={u'': u''},
**stats_dict
)
def _parse_to_filter_list(self, filter_expr):
"""Parse complex query expression to simple filter list.

View File

@ -616,20 +616,8 @@ class MeterStatisticsTest(_BaseTestCase):
def test_stats_list_with_groupby(self, mock_mdf):
with mock.patch("ceilometer.monasca_client.Client") as mock_client:
conn = impl_monasca.Connection("127.0.0.1:8080")
ml_mock = mock_client().metrics_list
ml_mock.return_value = [
{
'name': 'image',
'dimensions': {'project_id': '1234'}
},
{
'name': 'image',
'dimensions': {'project_id': '5678'}
}
]
sl_mock = mock_client().statistics_list
sl_mock.side_effect = [[
sl_mock.return_value = [
{
'statistics':
[
@ -639,8 +627,8 @@ class MeterStatisticsTest(_BaseTestCase):
],
'dimensions': {'project_id': '1234', 'unit': 'gb'},
'columns': ['timestamp', 'min', 'max', 'count', 'avg']
}],
[{
},
{
'statistics':
[
['2014-10-24T12:14:12Z', 0.45, 2.5, 2, 2.1],
@ -649,7 +637,7 @@ class MeterStatisticsTest(_BaseTestCase):
],
'dimensions': {'project_id': '5678', 'unit': 'gb'},
'columns': ['timestamp', 'min', 'max', 'count', 'avg']
}]]
}]
sf = storage.SampleFilter()
sf.meter = "image"