def insert_series_sample(data_service, val=None, *vals):
series = Series(ENTITY, METRIC)
series.tags = {TAG: TAG_VALUE}
series.add_samples(Sample(val, datetime.now()))
if vals:
for i, v in enumerate(vals):
time.sleep(WAIT_TIME + 2)
series.add_samples(Sample(v, datetime.now()))
# print('insertion =', series)
return data_service.insert(series)
def test_insert_retrieve_versioning(self):
test_status = 'pyapi.status'
now = datetime.now()
series = Series(ENTITY, VERSION_METRIC)
val = random.randint(0, VALUE - 1)
series.add_samples(
Sample(value=val,
time=now - timedelta(seconds=2),
version={'status': test_status}))
sf = SeriesFilter(metric=VERSION_METRIC)
ef = EntityFilter(entity=ENTITY)
df = DateFilter(start_date=now - timedelta(hours=1), end_date=now)
vf = VersioningFilter(versioned=True)
query = SeriesQuery(series_filter=sf,
entity_filter=ef,
date_filter=df,
versioning_filter=vf)
successful = self.service.insert(series)
time.sleep(WAIT_TIME)
series, = self.service.query(query)
# print(series)
last_sample = series.data[-1]
self.assertTrue(successful)
self.assertEqual(last_sample.v, val)
self.assertIsNotNone(last_sample.version)
self.assertEqual(last_sample.version['status'], test_status)
def test_fields_match(self):
sample = Sample(1, datetime.now())
series = Series(ENTITY, METRIC, tags={TAG: TAG_VALUE})
series.add_samples(sample)
self.assertEqual(ENTITY, series.entity)
self.assertEqual(METRIC, series.metric)
self.assertEqual({TAG: TAG_VALUE}, series.tags)
self.assertEqual([sample], series.data)
def main(filename,
atsd_url,
username,
password,
stat_as_tag,
entity_name=None,
timestamp=None):
if timestamp is None:
timestamp = datetime.now()
conn = connect_url(atsd_url, username, password)
series_service = SeriesService(conn)
with open(filename) as f:
entries = json.load(f)
for entry in entries:
benchmark_name_split = entry['benchmark'].split('.')
entity = benchmark_name_split[
-2] if entity_name is None else entity_name
metric_prefix = 'jmh.' + entry['mode'] + '.' + entry[
'primaryMetric']['scoreUnit']
tags = {'method': benchmark_name_split[-1]}
if not stat_as_tag:
metric = metric_prefix + '.avg'
else:
metric = metric_prefix
tags['stat'] = 'avg'
series_service.insert(
Series(entity, metric,
[Sample(entry['primaryMetric']['score'], timestamp)],
tags))
for key, value in entry['primaryMetric']['scorePercentiles'].items(
):
if key == '0.0':
stat = 'min'
elif key == '100.0':
stat = 'max'
else:
stat = key
if not stat_as_tag:
metric = metric_prefix + '.' + stat
else:
metric = metric_prefix
tags['stat'] = stat
series_service.insert(
Series(entity, metric, [Sample(value, timestamp)], tags))
def setUp(self):
"""
Insert series to open the alert.
"""
series = Series(ENTITY, METRIC)
series.add_samples(Sample(VALUE, datetime.now()))
self._series_service = SeriesService(self.connection)
self._series_service.insert(series)
time.sleep(self.wait_time)
def setUpClass(cls):
"""
Insert series.
"""
super().setUpClass()
series = Series(NAME, METRIC)
series.add_samples(Sample(1, datetime.now()))
series_service = SeriesService(cls.connection)
series_service.insert(series)
time.sleep(cls.wait_time)
def test_fields_match(self):
"""
Check fields of Series model were set as expected.
"""
sample = Sample(1, datetime.now())
series = Series(ENTITY, METRIC, tags={TAG: TAG_VALUE})
series.add_samples(sample)
self.assertEqual(ENTITY, series.entity)
self.assertEqual(METRIC, series.metric)
self.assertEqual({TAG: TAG_VALUE}, series.tags)
self.assertEqual([sample], series.data)
def test_history_query(self):
# Insert series to close the alert.
series = Series(ENTITY, METRIC)
series.add_samples(Sample(-1, datetime.now()))
self._series_service.insert(series)
time.sleep(self.wait_time)
query = AlertHistoryQuery(entity_filter=ef,
date_filter=df,
rule=RULE,
metric=METRIC)
result = self.service.history_query(query)
# print(result)
self.assertIsNotNone(result)
self.assertGreater(len(result), 0)
a = result[0]
self.common_checks(a)
def test_series(self):
# Insert series.
series = Series(ENTITY, NAME)
series.add_samples(Sample(1, datetime.now()))
series_service = SeriesService(self.connection)
series_service.insert(series)
time.sleep(self.wait_time)
result = self.service.series(metric=NAME,
min_insert_date='current_hour')
# print(result)
self.assertIsNotNone(result)
self.assertGreater(len(result), 0)
series = result[0]
self.assertIsNotNone(series)
self.assertIsInstance(series, Series)
self.assertEqual(NAME, series.metric)
self.assertEqual(ENTITY, series.entity)
self.assertEqual({}, series.tags)
def tearDown(self):
# Insert series to close the alert.
series = Series(ENTITY, METRIC)
series.add_samples(Sample(-1, datetime.now()))
self._series_service.insert(series)
time.sleep(self.wait_time)
batches_left = BATCH_COUNT
total_asset_duration[entity_name] = timedelta()
while batches_left > 0:
procedure_name = ''
if proc == 0:
stage_2_leaps, metrics = update_metrics_behaviour()
procedures, td = update_durations(stage_2_leaps)
total_asset_duration[entity_name] += timedelta(hours=td)
if SAVE_AS_COMMANDS:
commands.append(
'series e:%s x:%s=%s d:%s' % (entity_name, entity_name + UNIT_BATCH_ID_SUFFIX, batch_id, to_iso(t)))
else:
series.append(Series(entity_name, entity_name + UNIT_BATCH_ID_SUFFIX,
data=[Sample(time=t, x=batch_id, value=None)]))
batch_id += 1
procedure_name = 'Stage 1 Static Drying'
elif procedures[proc][0] == 'Inactive':
if SAVE_AS_COMMANDS:
commands.append(
'series e:%s x:%s=%s d:%s' % (
entity_name, entity_name + UNIT_BATCH_ID_SUFFIX, 'Inactive', to_iso(t)))
else:
series.append(Series(entity_name, entity_name + UNIT_BATCH_ID_SUFFIX,
data=[Sample(time=t, x='Inactive', value=None)]))
procedure_name = 'Inactive'
batches_left -= 1
elif procedures[proc][0] == 'Stage 2: Enable agitator 0':
procedure_name = 'Stage 2 Intermittent Agitation'
elif procedures[proc][0] == 'Stage 3: Product Cooled Down':
while batches_left > 0:
procedure_name = ''
if proc == 0:
stage_2_leaps, metrics = update_metrics_behaviour()
procedures, td = update_durations(stage_2_leaps)
total_asset_duration[entity_name] += timedelta(hours=td)
if SAVE_AS_COMMANDS:
commands.append('series e:%s x:%s=%s d:%s' %
(entity_name, entity_name +
UNIT_BATCH_ID_SUFFIX, batch_id, to_iso(t)))
else:
series.append(
Series(entity_name,
entity_name + UNIT_BATCH_ID_SUFFIX,
data=[Sample(time=t, x=batch_id, value=None)]))
batch_id += 1
procedure_name = 'Stage 1 Static Drying'
elif procedures[proc][0] == 'Inactive':
if SAVE_AS_COMMANDS:
commands.append('series e:%s x:%s=%s d:%s' %
(entity_name, entity_name +
UNIT_BATCH_ID_SUFFIX, 'Inactive', to_iso(t)))
else:
series.append(
Series(entity_name,
entity_name + UNIT_BATCH_ID_SUFFIX,
data=[Sample(time=t, x='Inactive', value=None)]))
procedure_name = 'Inactive'
batches_left -= 1
elif procedures[proc][0] == 'Stage 2: Enable agitator 0':
series = []
for asset in assets:
proc = 0
t = next_time(None, MIN_TIME)
dataSplines = SplineHolder()
dataCommandSplines.append([asset, dataSplines])
while t < MAX_TIME:
iso = t.isoformat()
if proc == 0:
series.append(
Series(asset['id'],
'axi.Unit_BatchID',
data=[Sample(time=iso, x=batch_id, value=None)]))
batch_id += 1
elif procedures[proc][0] == 'Inactive':
series.append(
Series(asset['id'],
'axi.Unit_BatchID',
data=[Sample(time=iso, x='Inactive', value=None)]))
series.append(
Series(asset['id'],
'axi.Unit_Procedure',
data=[Sample(time=iso, x=procedures[proc][0], value=None)]))
next_t = next_time(procedures[proc], t)
for [metric, splines] in metrics:
dataSplines.put_spline(t, next_t, metric,
splines[procedures[proc][0]])
proc = (proc + 1) % len(procedures)
start_date = '2018-01-01T00:00:00Z'
end_date = '2019-01-01T00:00:01Z'
# prepare series_query and execute it
sf = SeriesFilter(metric=metric_name)
ef = EntityFilter(entity=entity_name)
df = DateFilter(start_date=start_date, end_date=end_date)
query = SeriesQuery(series_filter=sf, entity_filter=ef, date_filter=df)
series_list = svc.query(query)
for series in series_list:
# update timestamps
updated_data = []
for sample in series.data:
current_date = sample.get_date()
for i in range(1, year_count + 1):
try:
# increment year
new_date = current_date.replace(year=current_date.year + i)
updated_data.append(
Sample(value=sample.v,
time=new_date,
version=sample.version))
except ValueError:
# Uncomment to add nonexistent dates to output
print('%s, year shift is %s' % (current_date, i))
continue
series.data = updated_data
series.aggregate = None
svc.insert(series)
