def test_detector_should_be_able_to_calculate_total_probability():
detector = Detector()
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
event = helpers.get_fake_event()
cpu_prob = detector.calculate_probability_by_metric("cpu", event)
mem_prob = detector.calculate_probability_by_metric("mem", event)
assert cpu_prob * mem_prob == detector.calculate_total_probability(event)
def test_detector_should_fetch_calculus_base_of_bucket_and_key():
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
models.add_event('edge', 'edge_02', {u'cpu': u'3', u'mem': u'20'}, time.time())
detector = Detector()
expected = (5, 2, 13)
assert expected == detector.fetch_data_from_calculus_base("edge", "cpu")
def test_should_update_calculus_base_when_event_created():
metrics = {u'load': u'2', u'mem': u'150'}
models.add_event('bk1', 'mach1', metrics, time.time())
assert_calculus_base_updated('bk1', {u'load': {'total': 2.0,
'count': 1,
'squared_total': 4.0},
u'mem': {'total': 150.0,
'count': 1,
'squared_total': 22500.0}
})
def test_detector_should_be_able_to_calculate_the_number_of_standard_deviations_of_metric():
# adding this two events will generate a standard deviation of: cpu: 0.5, mem: 5
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
models.add_event('edge', 'edge_02', {u'cpu': u'3', u'mem': u'20'}, time.time())
detector = Detector()
event = {
'target' : 'edge_01',
'bucket' : 'edge',
'metrics' : {'cpu': 4.0, 'mem': 30},
'timestamp': 8192819082,
}
assert 3 == detector.calculate_the_number_of_standard_deviations(event, "cpu")
assert 3 == detector.calculate_the_number_of_standard_deviations(event, "mem")
def test_should_update_calculus_base_on_different_buckets():
models.add_event('bk1', 'mach1', {u'load': u'2', u'mem': u'10'}, time.time())
models.add_event('bk2', 'mach2', {u'load': u'3', u'mem': u'20'}, time.time())
assert_calculus_base_updated('bk1', {u'load': {'total': 2.0,
'count': 1,
'squared_total': 4.0},
u'mem': {'total': 10.0,
'count': 1,
'squared_total': 100.0}
})
assert_calculus_base_updated('bk2', {u'load': {'total': 3.0,
'count': 1,
'squared_total': 9.0},
u'mem': {'total': 20.0,
'count': 1,
'squared_total': 400.0}
})
def test_should_not_duplicate_calculus_base_per_bucket():
models.add_event('bk1', 'mach1', {u'load': u'2', u'mem': u'10'}, time.time())
models.add_event('bk1', 'mach2', {u'load': u'3', u'mem': u'20'}, time.time())
models.add_event('bk1', 'mach3', {u'load': u'4', u'mem': u'30'}, time.time())
assert_calculus_base_updated('bk1', {u'load': {'total': 9.0,
'count': 3,
'squared_total': 29.0},
u'mem': {'total': 60.0,
'count': 3,
'squared_total': 1400.0}
})
def create_anomalous_event(bucket, target, metrics, timestamp):
event = models.add_event(bucket, target, metrics, timestamp)
models.mark_event_as_anomalous(event)
def create_event(self, *args, **kwargs):
try:
return models.add_event(*args, **kwargs)
except models.ValidationError as ex:
raise HTTPError(400, str(ex))
