def test_rating_cpu_frames(self):
labels = {
'instance_type': 'large',
'storage_type': 'hdd',
'gpu_accel': 'false'
}
_, rule = rules.find_match('request_cpu', labels, self.rules['rules'])
rating = rate(rule, {'qty': 42})
self.assertEqual(rating, 0.0378)
def test_matching_default(self):
labelset, rule = rules.find_match('usage_cpu', {
'cpu_arch': 'z80',
}, self.rules)
self.assertEqual({}, labelset)
self.assertEqual(
{
'metric': 'usage_cpu',
'value': 0.0002,
'unit': 'core-hours',
}, rule)
def test_matching_default_with_checked_label(self):
labelset, rule = rules.find_match('usage_cpu', {
'instance_type': 'foobar',
}, self.rules)
self.assertEqual({}, labelset)
self.assertEqual(
{
'metric': 'usage_cpu',
'value': 0.0002,
'unit': 'core-hours',
}, rule)
def test_matching_labels(self):
labelset, rule = rules.find_match('usage_cpu', {
'instance_type': 'small',
'sldjalfksdjf': 'alsdkfjalsdkfj'
}, self.rules)
self.assertEqual({'instance_type': 'small'}, labelset)
self.assertEqual(
{
'metric': 'usage_cpu',
'value': 0.0015,
'unit': 'core-hours',
}, rule)
def test_rating_no_match(self):
_, rule = rules.find_match('pokemon', {}, self.rules['rules'])
rating = rate(rule, {'qty': 42})
self.assertEqual(rating, None)
def test_matching_wrong_metric(self):
labelset, rule = rules.find_match('nothing', {
'instance_type': 'pokemon',
}, self.rules)
self.assertEqual({}, labelset)
self.assertEqual({}, rule)
def retrieve_data(rules: Dict, metric_config: Dict, logger: Logger):
"""
Retrieve and rate data according to rules and metrics configuration.
:rules (Dict) A dictionary holding the rules to rate the frames.
:metric_config (Dict) A dictionary holding the metrics configuration.
"""
logger.info(f'Loading frames from {metric_config["presto_table"]}..')
logger.info('checking for labels..')
labels_name = get_labels_from_table(metric_config['presto_table'],
metric_config['presto_column'])
potential_labels = ', '.join(labels_name)
if len(potential_labels) > 0:
logger.info(f'found labels: {potential_labels}')
potential_labels = f', {potential_labels}'
else:
logger.info('no labels found')
frames = get_frames(metric_config, potential_labels)
loaded = len(frames)
if loaded == 0:
logger.info('no frames loaded')
return
logger.info(f'{loaded} frames loaded')
rated_frames, rated_namespaces = [], []
rating_time = dt.utcnow()
for frame in frames:
# 6 here because every columns after is considered a label
frame_labels = extract_frames_labels(frame,
metric_config['presto_column'],
labels_name)
labels, rule = rs.find_match(metric_config['metric'], frame_labels,
rules)
converted = rates.convert_metrics_unit(
metric_config['unit'], rule['unit'],
frame[metric_config['presto_column']])
rated_frames.append((
frame['period_start'], # frame_begin
frame['period_end'], # frame_end
frame['namespace'], # namespace
frame['node'], # node
metric_config['metric'], # metric
frame['pod'], # pod
converted, # quantity
rates.rate(rule, {'qty': converted}), # rating
f'{labels}'))
rated_namespace = frame['namespace']
if rated_namespace not in rated_namespaces:
rated_namespaces.append(rated_namespace)
logger.info('frame processed')
logger.info('sending data..')
result = update_rated_data(
rated_frames, rated_namespaces, metric_config,
rating_time.isoformat(sep=' ', timespec='milliseconds'))
if result:
logger.info(
f'updated rated-{metric_config["metric"].replace("_", "-")} object'
)
logger.info('finished rating instance')