def test_update(self, time_mock):
time_mock.return_value = 123
observer = LastValueAggregator()
# test current values without any update
self.assertIsNone(observer.current)
# call update with some values
values = (3, 50, 3, 97, 27)
for val in values:
observer.update(val)
self.assertEqual(observer.last_update_timestamp, 123)
self.assertEqual(observer.current, values[-1])
def test_merge_last_updated_none(self):
observer1 = LastValueAggregator()
observer2 = LastValueAggregator()
observer1.checkpoint = 23
observer2.checkpoint = 47
observer1.last_update_timestamp = 0
observer2.last_update_timestamp = 100
observer1.merge(observer2)
self.assertEqual(observer1.checkpoint, 47)
self.assertEqual(observer1.last_update_timestamp, 100)
def test_checkpoint(self):
observer = LastValueAggregator()
# take checkpoint without any update
observer.take_checkpoint()
self.assertEqual(observer.checkpoint, None)
# call update with some values
values = (3, 50, 3, 97)
for val in values:
observer.update(val)
observer.take_checkpoint()
self.assertEqual(observer.checkpoint, 97)
def test_merge_with_empty(self):
observer1 = LastValueAggregator()
observer2 = LastValueAggregator()
observer1.checkpoint = 23
observer1.last_update_timestamp = 100
observer1.merge(observer2)
self.assertEqual(observer1.checkpoint, 23)
self.assertEqual(observer1.last_update_timestamp, 100)
def test_convert_from_last_value(self):
last_value_record = ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
LastValueAggregator(),
Resource({}),
)
last_value_record.aggregator.update(1)
last_value_record.aggregator.update(5)
last_value_record.aggregator.take_checkpoint()
expected_timeseries = self.exporter._create_timeseries(
last_value_record, "testname_last", 5.0)
timeseries = self.exporter._convert_from_last_value(last_value_record)
self.assertEqual(timeseries[0], expected_timeseries)
def aggregator_for(self, instrument_type: Type[InstrumentT]) -> Aggregator:
"""Returns an aggregator based on metric instrument type.
Aggregators keep track of and updates values when metrics get updated.
"""
# pylint:disable=R0201
if issubclass(instrument_type, (Counter, UpDownCounter)):
return SumAggregator()
if issubclass(instrument_type, (SumObserver, UpDownSumObserver)):
return LastValueAggregator()
if issubclass(instrument_type, ValueRecorder):
return MinMaxSumCountAggregator()
if issubclass(instrument_type, ValueObserver):
return ValueObserverAggregator()
# TODO: Add other aggregators
return SumAggregator()
def test_valid_convert_to_timeseries(self):
test_records = [
ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
SumAggregator(),
Resource({}),
),
ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
MinMaxSumCountAggregator(),
Resource({}),
),
ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
HistogramAggregator(),
Resource({}),
),
ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
LastValueAggregator(),
Resource({}),
),
ExportRecord(
Counter("testname", "testdesc", "testunit", int, None),
None,
ValueObserverAggregator(),
Resource({}),
),
]
for record in test_records:
record.aggregator.update(5)
record.aggregator.take_checkpoint()
data = self.exporter._convert_to_timeseries(test_records)
self.assertIsInstance(data, list)
self.assertEqual(len(data), 13)
for timeseries in data:
self.assertIsInstance(timeseries, TimeSeries)