def test_build_allocations_from_rules(rules, expected_tasks_allocations):
tasks_data = {
't1_task_id': task_data('/t1', labels={'foo': 'bar'}),
't2_task_id': task_data('/t1')
}
assert _build_allocations_from_rules(tasks_data,
rules) == expected_tasks_allocations
def test_static_allocator(allocate_according_rules_mock, load_config_mock):
static_allocator = StaticAllocator(config='somefile',
rules=[{
'allocations': {
'cpu_quota': 0.5
}
}])
platform_mock = Mock()
tasks_data = {
't1_task_id':
task_data('/t1', labels={'foo': 'bar'}, resources={'cpu_quota': 1.0})
}
assert static_allocator.allocate(platform_mock, tasks_data) == ({}, [], [])
allocate_according_rules_mock.assert_called_once()
load_config_mock.assert_called_once()
platform_mock,
allocation_configuration=AllocationConfiguration(
cpu_quota_period=1000))
}
assert {} == _get_tasks_allocations(containers)
@pytest.mark.parametrize(
'allocations, tasks_data, expected',
(({
't1_task_id': {
AllocationType.SHARES: 10
}
}, {
't1_task_id': task_data('/t1')
}, {
't1_task_id': task_data('/t1', allocations={AllocationType.SHARES: 10})
}), ({
't1_task_id': {
AllocationType.SHARES: 10
},
't2_task_id': {
AllocationType.SHARES: 20
}
}, {
't1_task_id': task_data('/t1')
}, {
't1_task_id':
task_data('/t1', allocations={AllocationType.SHARES: 10})
}), ({}, {
runner._iterate()
sleep_mock.assert_called_with(0.7)
assert runner._last_iteration == 1.3
with patch('time.time', return_value=2.5):
runner._iterate()
sleep_mock.assert_called_with(0)
_task_cpu_usage_metadata = METRICS_METADATA[MetricName.TASK_CPU_USAGE_SECONDS]
@pytest.mark.parametrize(
'tasks_data, expected_metrics',
[({}, []), ({
't1_task_id': task_data('/t1', labels={'app': 'redis'})
}, []),
({
't1_task_id':
task_data(
'/t1',
labels={'app': 'redis'},
measurements={'task_cpu_usage_seconds': DEFAULT_METRIC_VALUE})
}, [
Metric(MetricName.TASK_CPU_USAGE_SECONDS,
labels={'app': 'redis'},
value=DEFAULT_METRIC_VALUE,
unit=_task_cpu_usage_metadata.unit,
granularity=_task_cpu_usage_metadata.granularity,
help=_task_cpu_usage_metadata.help,
type=_task_cpu_usage_metadata.type)
# 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 pytest
from wca.detectors import convert_anomalies_to_metrics
from tests.testing import anomaly, anomaly_metrics, task_data
@pytest.mark.parametrize('anomalies,tasks_data,expected_metrics', (
([], {}, []),
([anomaly('t1', ['t2'])], {
't1':
task_data('/t1', labels={'workload_instance': 't1_workload_instance'}),
't2':
task_data('/t2', labels={'workload_instance': 't2_workload_instance'})
},
anomaly_metrics('t1', ['t2'], {
't1': 't1_workload_instance',
't2': 't2_workload_instance'
})),
([anomaly('t2', ['t1', 't3'])], {
't1':
task_data('/t1', labels={'workload_instance': 't1_workload_instance'}),
't2':
task_data('/t2', labels={'workload_instance': 't2_workload_instance'}),
't3':
task_data('/t3', labels={'workload_instance': 't3_workload_instance'})
},