def collect(self) -> Optional[Sum]:
"""
Atomically return a point for the current value of the metric and
reset the aggregation value.
"""
now = _time_ns()
if self._instrument_temporality is AggregationTemporality.DELTA:
with self._lock:
value = self._value
start_time_unix_nano = self._start_time_unix_nano
self._value = 0
self._start_time_unix_nano = now + 1
return Sum(
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=self._instrument_is_monotonic,
start_time_unix_nano=start_time_unix_nano,
time_unix_nano=now,
value=value,
)
if self._value is None:
return None
return Sum(
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=self._instrument_is_monotonic,
start_time_unix_nano=self._start_time_unix_nano,
time_unix_nano=now,
value=self._value,
)
def test_current_point_sum_current_point_same_aggregation_temporality(
self, ):
current_point = Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
)
self.assertEqual(
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
),
current_point,
AggregationTemporality.DELTA,
),
current_point,
)
current_point = Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
)
self.assertEqual(
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
),
current_point,
AggregationTemporality.CUMULATIVE,
),
current_point,
)
def _generate_sum(name, val) -> Sum:
return _generate_metric(
name,
Sum(
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=True,
start_time_unix_nano=1641946015139533244,
time_unix_nano=1641946016139533244,
value=val,
),
)
def test_previous_point_non_cumulative(self):
with self.assertRaises(Exception):
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
),
Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
),
AggregationTemporality.DELTA,
),
def test_current_point_sum_previous_point_none(self):
current_point = Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
)
self.assertEqual(
_convert_aggregation_temporality(
None, current_point, AggregationTemporality.CUMULATIVE),
replace(
current_point,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
),
)
def test_mismatched_point_types(self):
current_point = Sum(
start_time_unix_nano=0,
time_unix_nano=0,
value=0,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
)
with self.assertLogs(level=WARNING):
self.assertIs(
_convert_aggregation_temporality(
Gauge(time_unix_nano=0, value=0),
current_point,
AggregationTemporality.DELTA,
),
current_point,
)
def _convert_aggregation_temporality(
previous_point: Optional[_PointVarT],
current_point: _PointVarT,
aggregation_temporality: AggregationTemporality,
) -> _PointVarT:
"""Converts `current_point` to the requested `aggregation_temporality`
given the `previous_point`.
`previous_point` must have `CUMULATIVE` temporality. `current_point` may
have `DELTA` or `CUMULATIVE` temporality.
The output point will have temporality `aggregation_temporality`. Since
`GAUGE` points have no temporality, they are returned unchanged.
"""
current_point_type = type(current_point)
if current_point_type is Gauge:
return current_point
if previous_point is not None and type(previous_point) is not type(
current_point):
_logger.warning(
"convert_aggregation_temporality called with mismatched "
"point types: %s and %s",
type(previous_point),
current_point_type,
)
return current_point
if current_point_type is Sum:
if previous_point is None:
# Output CUMULATIVE for a synchronous instrument
# There is no previous value, return the delta point as a
# cumulative
return replace(current_point,
aggregation_temporality=aggregation_temporality)
if previous_point.aggregation_temporality is not (
AggregationTemporality.CUMULATIVE):
raise Exception(
"previous_point aggregation temporality must be CUMULATIVE")
if current_point.aggregation_temporality is aggregation_temporality:
# Output DELTA for a synchronous instrument
# Output CUMULATIVE for an asynchronous instrument
return current_point
if aggregation_temporality is AggregationTemporality.DELTA:
# Output temporality DELTA for an asynchronous instrument
value = current_point.value - previous_point.value
output_start_time_unix_nano = previous_point.time_unix_nano
else:
# Output CUMULATIVE for a synchronous instrument
value = current_point.value + previous_point.value
output_start_time_unix_nano = previous_point.start_time_unix_nano
is_monotonic = (previous_point.is_monotonic
and current_point.is_monotonic)
return Sum(
start_time_unix_nano=output_start_time_unix_nano,
time_unix_nano=current_point.time_unix_nano,
value=value,
aggregation_temporality=aggregation_temporality,
is_monotonic=is_monotonic,
)
return None
def test_current_point_sum_aggregation_temporality_cumulative(self):
self.assertEqual(
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=1,
time_unix_nano=2,
value=3,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
),
Sum(
start_time_unix_nano=4,
time_unix_nano=5,
value=6,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
),
AggregationTemporality.CUMULATIVE,
),
Sum(
start_time_unix_nano=1,
time_unix_nano=5,
value=9,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
),
)
self.assertEqual(
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=1,
time_unix_nano=2,
value=3,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=True,
),
Sum(
start_time_unix_nano=4,
time_unix_nano=5,
value=6,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=False,
),
AggregationTemporality.CUMULATIVE,
),
Sum(
start_time_unix_nano=1,
time_unix_nano=5,
value=9,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=False,
),
)
self.assertEqual(
_convert_aggregation_temporality(
Sum(
start_time_unix_nano=1,
time_unix_nano=2,
value=3,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=True,
),
Sum(
start_time_unix_nano=4,
time_unix_nano=5,
value=6,
aggregation_temporality=AggregationTemporality.DELTA,
is_monotonic=True,
),
AggregationTemporality.CUMULATIVE,
),
Sum(
start_time_unix_nano=1,
time_unix_nano=5,
value=9,
aggregation_temporality=AggregationTemporality.CUMULATIVE,
is_monotonic=True,
),
)
def shutdown(self):
self._shutdown = True
metrics_list = [
Metric(
name="sum_name",
attributes={},
description="",
instrumentation_info=None,
resource=Resource.create(),
unit="",
point=Sum(
start_time_unix_nano=_time_ns(),
time_unix_nano=_time_ns(),
value=2,
aggregation_temporality=1,
is_monotonic=True,
),
),
Metric(
name="gauge_name",
attributes={},
description="",
instrumentation_info=None,
resource=Resource.create(),
unit="",
point=Gauge(
time_unix_nano=_time_ns(),
value=2,
),