bd8636f3f0
Allows to define a global preference for metric datasources with the ability for strategy specific overrides. In addition, strategies which do not require datasources have the config options removed this is done to prevent confusion. Some documentation that details the inner workings of selecting datasources is updated. Imports for some files in watcher/common have been changed to resolve circular dependencies and now match the overall method to import configuration. Addtional datasources will be retrieved by the manager if the datasource throws an error. Implements: blueprint global-datasource-preference Change-Id: I6fc455b288e338c20d2c4cfec5a0c95350bebc36
130 lines
5.0 KiB
Python
130 lines
5.0 KiB
Python
# -*- encoding: utf-8 -*-
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# Copyright (c) 2015 b<>com
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain 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,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import mock
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from oslo_config import cfg
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from watcher.common import clients
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from watcher.datasources import monasca as monasca_helper
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from watcher.tests import base
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CONF = cfg.CONF
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@mock.patch.object(clients.OpenStackClients, 'monasca')
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class TestMonascaHelper(base.BaseTestCase):
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def test_monasca_statistic_aggregation(self, mock_monasca):
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monasca = mock.MagicMock()
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expected_stat = [{
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'columns': ['timestamp', 'avg'],
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'dimensions': {
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'hostname': 'rdev-indeedsrv001',
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'service': 'monasca'},
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'id': '0',
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'name': 'cpu.percent',
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'statistics': [
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['2016-07-29T12:45:00Z', 0.0],
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['2016-07-29T12:50:00Z', 0.9],
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['2016-07-29T12:55:00Z', 0.9]]}]
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monasca.metrics.list_statistics.return_value = expected_stat
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mock_monasca.return_value = monasca
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helper = monasca_helper.MonascaHelper()
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result = helper.statistic_aggregation(
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resource_id=None,
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meter_name='cpu.percent',
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period=7200,
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granularity=300,
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dimensions={'hostname': 'NODE_UUID'},
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aggregation='avg',
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group_by='*',
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)
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self.assertEqual(0.6, result)
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def test_check_availability(self, mock_monasca):
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monasca = mock.MagicMock()
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monasca.metrics.list.return_value = True
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mock_monasca.return_value = monasca
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helper = monasca_helper.MonascaHelper()
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result = helper.check_availability()
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self.assertEqual('available', result)
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def test_check_availability_with_failure(self, mock_monasca):
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monasca = mock.MagicMock()
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monasca.metrics.list.side_effect = Exception()
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mock_monasca.return_value = monasca
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helper = monasca_helper.MonascaHelper()
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self.assertEqual('not available', helper.check_availability())
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def test_monasca_statistic_list(self, mock_monasca):
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monasca = mock.MagicMock()
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expected_result = [{
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'columns': ['timestamp', 'value', 'value_meta'],
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'dimensions': {
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'hostname': 'rdev-indeedsrv001',
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'service': 'monasca'},
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'id': '0',
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'measurements': [
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['2016-07-29T12:54:06.000Z', 0.9, {}],
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['2016-07-29T12:54:36.000Z', 0.9, {}],
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['2016-07-29T12:55:06.000Z', 0.9, {}],
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['2016-07-29T12:55:36.000Z', 0.8, {}]],
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'name': 'cpu.percent'}]
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monasca.metrics.list_measurements.return_value = expected_result
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mock_monasca.return_value = monasca
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helper = monasca_helper.MonascaHelper()
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val = helper.statistics_list(meter_name="cpu.percent", dimensions={})
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self.assertEqual(expected_result, val)
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def test_monasca_statistic_list_query_retry(self, mock_monasca):
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monasca = mock.MagicMock()
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expected_result = [{
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'columns': ['timestamp', 'value', 'value_meta'],
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'dimensions': {
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'hostname': 'rdev-indeedsrv001',
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'service': 'monasca'},
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'id': '0',
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'measurements': [
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['2016-07-29T12:54:06.000Z', 0.9, {}],
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['2016-07-29T12:54:36.000Z', 0.9, {}],
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['2016-07-29T12:55:06.000Z', 0.9, {}],
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['2016-07-29T12:55:36.000Z', 0.8, {}]],
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'name': 'cpu.percent'}]
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monasca.metrics.list_measurements.side_effect = [expected_result]
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mock_monasca.return_value = monasca
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helper = monasca_helper.MonascaHelper()
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val = helper.statistics_list(meter_name="cpu.percent", dimensions={})
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self.assertEqual(expected_result, val)
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@mock.patch.object(monasca_helper.MonascaHelper, 'statistic_aggregation')
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def test_get_host_cpu_usage(self, mock_aggregation, mock_monasca):
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node = "compute1_compute1"
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mock_aggregation.return_value = 0.6
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helper = monasca_helper.MonascaHelper()
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cpu_usage = helper.get_host_cpu_usage(node, 600, 'mean')
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self.assertEqual(0.6, cpu_usage)
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@mock.patch.object(monasca_helper.MonascaHelper, 'statistic_aggregation')
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def test_get_instance_cpu_usage(self, mock_aggregation, mock_monasca):
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mock_aggregation.return_value = 0.6
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helper = monasca_helper.MonascaHelper()
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cpu_usage = helper.get_instance_cpu_usage('vm1', 600, 'mean')
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self.assertEqual(0.6, cpu_usage)
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