def test_measurements_runner(subcgroups):
# Node mock
t1 = redis_task_with_default_labels('t1', subcgroups)
t2 = redis_task_with_default_labels('t2', subcgroups)
runner = MeasurementRunner(
node=Mock(spec=MesosNode, get_tasks=Mock(return_value=[t1, t2])),
metrics_storage=Mock(spec=storage.Storage, store=Mock()),
rdt_enabled=False,
extra_labels=dict(
extra_label='extra_value') # extra label with some extra value
)
runner._wait = Mock()
# Mock to finish after one iteration.
runner._initialize()
runner._iterate()
# Check output metrics.
got_metrics = runner._metrics_storage.store.call_args[0][0]
# Internal wca metrics are generated (wca is running, number of task under control,
# memory usage and profiling information)
assert_metric(got_metrics, 'wca_up', dict(extra_label='extra_value'))
assert_metric(got_metrics, 'wca_tasks', expected_metric_value=2)
# wca & its children memory usage (in bytes)
assert_metric(got_metrics,
'wca_memory_usage_bytes',
expected_metric_value=WCA_MEMORY_USAGE * 2 * 1024)
# Measurements metrics about tasks, based on get_measurements mocks.
cpu_usage = TASK_CPU_USAGE * (len(subcgroups) if subcgroups else 1)
assert_metric(got_metrics,
'cpu_usage',
dict(task_id=t1.task_id),
expected_metric_value=cpu_usage)
assert_metric(got_metrics,
'cpu_usage',
dict(task_id=t2.task_id),
expected_metric_value=cpu_usage)
# Test whether application and application_version_name were properly generated using
# default runner._task_label_generators defined in constructor of MeasurementsRunner.
assert_metric(got_metrics, 'cpu_usage', {
'application': t1.name,
'application_version_name': ''
})
# Test whether `initial_task_cpu_assignment` label is attached to task metrics.
assert_metric(got_metrics, 'cpu_usage',
{'initial_task_cpu_assignment': '8.0'})
def test_measurements_runner(subcgroups):
# Node mock
t1 = redis_task_with_default_labels('t1', subcgroups)
t2 = redis_task_with_default_labels('t2', subcgroups)
runner = MeasurementRunner(node=Mock(
spec=MesosNode, get_tasks=Mock(return_value=[t1, t2])),
metrics_storage=Mock(spec=storage.Storage,
store=Mock()),
rdt_enabled=False,
gather_hw_mm_topology=False,
extra_labels=dict(extra_label='extra_value'))
runner._wait = Mock()
# Mock to finish after one iteration.
runner._initialize()
runner._iterate()
# Check output metrics.
got_metrics = runner._metrics_storage.store.call_args[0][0]
# Internal wca metrics are generated (wca is running, number of task under control,
# memory usage and profiling information)
assert_metric(got_metrics, MetricName.WCA_UP,
dict(extra_label='extra_value'))
assert_metric(got_metrics, MetricName.WCA_TASKS, expected_metric_value=2)
# wca & its children memory usage (in bytes)
assert_metric(got_metrics,
MetricName.WCA_MEM_USAGE_BYTES,
expected_metric_value=WCA_MEMORY_USAGE * 2 * 1024)
# Measurements metrics about tasks, based on get_measurements mocks.
cpu_usage = TASK_CPU_USAGE * (len(subcgroups) if subcgroups else 1)
assert_metric(got_metrics,
MetricName.TASK_CPU_USAGE_SECONDS,
dict(task_id=t1.task_id),
expected_metric_value=cpu_usage)
assert_metric(got_metrics,
MetricName.TASK_CPU_USAGE_SECONDS,
dict(task_id=t2.task_id),
expected_metric_value=cpu_usage)
def test_measurements_wait(sleep_mock):
with patch('time.time', return_value=1):
runner = MeasurementRunner(node=Mock(spec=MesosNode,
get_tasks=Mock(return_value=[])),
metrics_storage=Mock(spec=storage.Storage,
store=Mock()),
rdt_enabled=False,
extra_labels={})
runner._initialize()
runner._iterate()
sleep_mock.assert_called_once_with(1.0)
with patch('time.time', return_value=1.3):
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)