def test_aggregate_series(self):
val = random.randint(0, VALUE - 1)
insert_series_sample(self.service, val, val + 1)
time.sleep(WAIT_TIME)
now = datetime.now()
sf = SeriesFilter(metric=METRIC, tags={TAG: [TAG_VALUE]})
ef = EntityFilter(entity=ENTITY)
df = DateFilter(start_date=now - timedelta(hours=1), end_date=now)
aggr = Aggregate(period={
'count': 10,
'unit': TimeUnit.SECOND
},
types=[AggregateType.MAX, AggregateType.MIN])
tf = TransformationFilter(aggregate=aggr)
query = SeriesQuery(series_filter=sf,
entity_filter=ef,
date_filter=df,
transformation_filter=tf)
series = self.service.query(query)
self.assertEqual(len(series), 2)
if series[0].aggregate['type'] == 'MAX':
max = series[0].get_last_value()
min = series[1].get_last_value()
else:
min = series[0].get_last_value()
max = series[1].get_last_value()
self.assertGreaterEqual(max, min)
metrics = entities_service.metrics(args.entity, expression=metric_expression,
min_insert_date=now - timedelta(seconds=args.last_hours * 3600),
use_entity_insert_time=True)
log('Processing: ')
for metric in metrics:
sf = SeriesFilter(metric=metric.name)
ef = EntityFilter(entity=args.entity)
df = DateFilter(start_date=datetime(now.year, now.month, now.day) - timedelta(days=args.data_interval),
end_date='now')
tf = TransformationFilter()
query = SeriesQuery(series_filter=sf, entity_filter=ef, date_filter=df)
if args.aggregate_period > 0:
tf.set_aggregate(
Aggregate(period={'count': args.aggregate_period, 'unit': TimeUnit.MINUTE}, types=[AggregateType.MEDIAN]))
if args.interpolate_period > 0:
tf.set_interpolate(Interpolate(period={'count': args.interpolate_period, 'unit': TimeUnit.MINUTE},
function=InterpolateFunction.LINEAR))
query.set_transformation_filter(tf)
series_list = svc.query(query)
for series in series_list:
metric_id = '- %s %s' % (series.metric, print_tags(series.tags))
log('\t' + metric_id)
# exclude empty series for specific tags
if len(series.data) > 2:
ts = {int(sample.t / 1000): sample.v for sample in series.data}
with open(filename) as fp:
line = fp.readline()
while line:
series = Series.from_dict(ast.literal_eval(line))
expected_series.extend(series)
line = fp.readline()
# prepare series query
sf = SeriesFilter(metric=metric_name, tags=tags, exact_match=exact_match)
ef = EntityFilter(entity=entity_name)
df = DateFilter(start_date=start_date, end_date=end_date)
aggregate = Aggregate(period={
'count': 7,
'unit': TimeUnit.MINUTE
},
threshold={
'min': 10,
'max': 90
},
types=aggregate_types,
order=1)
tf = TransformationFilter(aggregate=aggregate)
# add rate and group to the transformation filter if specified instead of query
if query != 'query':
for attr in query.split('+'):
if attr == 'rate':
tf.set_rate(Rate(period={'count': 3, 'unit': TimeUnit.MINUTE}))
elif attr == 'group':
tf.set_group(
Group(type=AggregateType.SUM,
truncate=True,