# Copyright The OpenTelemetry Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=too-many-lines from abc import ABC, abstractmethod from bisect import bisect_left from enum import IntEnum from functools import partial from logging import getLogger from math import inf from threading import Lock from typing import Callable, Generic, List, Optional, Sequence, Type, TypeVar from opentelemetry.metrics import ( Asynchronous, Counter, Histogram, Instrument, ObservableCounter, ObservableGauge, ObservableUpDownCounter, Synchronous, UpDownCounter, _Gauge, ) from opentelemetry.sdk.metrics._internal.exemplar import ( Exemplar, ExemplarReservoirBuilder, ) from opentelemetry.sdk.metrics._internal.exponential_histogram.buckets import ( Buckets, ) from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping import ( Mapping, ) from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.exponent_mapping import ( ExponentMapping, ) from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.logarithm_mapping import ( LogarithmMapping, ) from opentelemetry.sdk.metrics._internal.measurement import Measurement from opentelemetry.sdk.metrics._internal.point import Buckets as BucketsPoint from opentelemetry.sdk.metrics._internal.point import ( ExponentialHistogramDataPoint, HistogramDataPoint, NumberDataPoint, Sum, ) from opentelemetry.sdk.metrics._internal.point import Gauge as GaugePoint from opentelemetry.sdk.metrics._internal.point import ( Histogram as HistogramPoint, ) from opentelemetry.util.types import Attributes _DataPointVarT = TypeVar("_DataPointVarT", NumberDataPoint, HistogramDataPoint) _logger = getLogger(__name__) class AggregationTemporality(IntEnum): """ The temporality to use when aggregating data. Can be one of the following values: """ UNSPECIFIED = 0 DELTA = 1 CUMULATIVE = 2 class _Aggregation(ABC, Generic[_DataPointVarT]): def __init__( self, attributes: Attributes, reservoir_builder: ExemplarReservoirBuilder, ): self._lock = Lock() self._attributes = attributes self._reservoir = reservoir_builder() self._previous_point = None @abstractmethod def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ) -> None: """Aggregate a measurement. Args: measurement: Measurement to aggregate should_sample_exemplar: Whether the measurement should be sampled by the exemplars reservoir or not. """ @abstractmethod def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[_DataPointVarT]: pass def _collect_exemplars(self) -> Sequence[Exemplar]: """Returns the collected exemplars. Returns: The exemplars collected by the reservoir """ return self._reservoir.collect(self._attributes) def _sample_exemplar( self, measurement: Measurement, should_sample_exemplar: bool ) -> None: """Offer the measurement to the exemplar reservoir for sampling. It should be called within the each :ref:`aggregate` call. Args: measurement: The new measurement should_sample_exemplar: Whether the measurement should be sampled by the exemplars reservoir or not. """ if should_sample_exemplar: self._reservoir.offer( measurement.value, measurement.time_unix_nano, measurement.attributes, measurement.context, ) class _DropAggregation(_Aggregation): def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ) -> None: pass def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[_DataPointVarT]: pass class _SumAggregation(_Aggregation[Sum]): def __init__( self, attributes: Attributes, instrument_is_monotonic: bool, instrument_aggregation_temporality: AggregationTemporality, start_time_unix_nano: int, reservoir_builder: ExemplarReservoirBuilder, ): super().__init__(attributes, reservoir_builder) self._start_time_unix_nano = start_time_unix_nano self._instrument_aggregation_temporality = ( instrument_aggregation_temporality ) self._instrument_is_monotonic = instrument_is_monotonic self._value = None self._previous_collection_start_nano = self._start_time_unix_nano self._previous_value = 0 def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ) -> None: with self._lock: if self._value is None: self._value = 0 self._value = self._value + measurement.value self._sample_exemplar(measurement, should_sample_exemplar) def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[NumberDataPoint]: """ Atomically return a point for the current value of the metric and reset the aggregation value. Synchronous instruments have a method which is called directly with increments for a given quantity: For example, an instrument that counts the amount of passengers in every vehicle that crosses a certain point in a highway: synchronous_instrument.add(2) collect(...) # 2 passengers are counted synchronous_instrument.add(3) collect(...) # 3 passengers are counted synchronous_instrument.add(1) collect(...) # 1 passenger is counted In this case the instrument aggregation temporality is DELTA because every value represents an increment to the count, Asynchronous instruments have a callback which returns the total value of a given quantity: For example, an instrument that measures the amount of bytes written to a certain hard drive: callback() -> 1352 collect(...) # 1352 bytes have been written so far callback() -> 2324 collect(...) # 2324 bytes have been written so far callback() -> 4542 collect(...) # 4542 bytes have been written so far In this case the instrument aggregation temporality is CUMULATIVE because every value represents the total of the measurement. There is also the collection aggregation temporality, which is passed to this method. The collection aggregation temporality defines the nature of the returned value by this aggregation. When the collection aggregation temporality matches the instrument aggregation temporality, then this method returns the current value directly: synchronous_instrument.add(2) collect(DELTA) -> 2 synchronous_instrument.add(3) collect(DELTA) -> 3 synchronous_instrument.add(1) collect(DELTA) -> 1 callback() -> 1352 collect(CUMULATIVE) -> 1352 callback() -> 2324 collect(CUMULATIVE) -> 2324 callback() -> 4542 collect(CUMULATIVE) -> 4542 When the collection aggregation temporality does not match the instrument aggregation temporality, then a conversion is made. For this purpose, this aggregation keeps a private attribute, self._previous_value. When the instrument is synchronous: self._previous_value is the sum of every previously collected (delta) value. In this case, the returned (cumulative) value will be: self._previous_value + value synchronous_instrument.add(2) collect(CUMULATIVE) -> 2 synchronous_instrument.add(3) collect(CUMULATIVE) -> 5 synchronous_instrument.add(1) collect(CUMULATIVE) -> 6 Also, as a diagram: time -> self._previous_value |-------------| value (delta) |----| returned value (cumulative) |------------------| When the instrument is asynchronous: self._previous_value is the value of the previously collected (cumulative) value. In this case, the returned (delta) value will be: value - self._previous_value callback() -> 1352 collect(DELTA) -> 1352 callback() -> 2324 collect(DELTA) -> 972 callback() -> 4542 collect(DELTA) -> 2218 Also, as a diagram: time -> self._previous_value |-------------| value (cumulative) |------------------| returned value (delta) |----| """ with self._lock: value = self._value self._value = None if ( self._instrument_aggregation_temporality is AggregationTemporality.DELTA ): # This happens when the corresponding instrument for this # aggregation is synchronous. if ( collection_aggregation_temporality is AggregationTemporality.DELTA ): previous_collection_start_nano = ( self._previous_collection_start_nano ) self._previous_collection_start_nano = ( collection_start_nano ) if value is None: return None return NumberDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=previous_collection_start_nano, time_unix_nano=collection_start_nano, value=value, ) if value is None: value = 0 self._previous_value = value + self._previous_value return NumberDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=self._start_time_unix_nano, time_unix_nano=collection_start_nano, value=self._previous_value, ) # This happens when the corresponding instrument for this # aggregation is asynchronous. if value is None: # This happens when the corresponding instrument callback # does not produce measurements. return None if ( collection_aggregation_temporality is AggregationTemporality.DELTA ): result_value = value - self._previous_value self._previous_value = value previous_collection_start_nano = ( self._previous_collection_start_nano ) self._previous_collection_start_nano = collection_start_nano return NumberDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=previous_collection_start_nano, time_unix_nano=collection_start_nano, value=result_value, ) return NumberDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=self._start_time_unix_nano, time_unix_nano=collection_start_nano, value=value, ) class _LastValueAggregation(_Aggregation[GaugePoint]): def __init__( self, attributes: Attributes, reservoir_builder: ExemplarReservoirBuilder, ): super().__init__(attributes, reservoir_builder) self._value = None def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ): with self._lock: self._value = measurement.value self._sample_exemplar(measurement, should_sample_exemplar) def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[_DataPointVarT]: """ Atomically return a point for the current value of the metric. """ with self._lock: if self._value is None: return None value = self._value self._value = None exemplars = self._collect_exemplars() return NumberDataPoint( attributes=self._attributes, exemplars=exemplars, start_time_unix_nano=None, time_unix_nano=collection_start_nano, value=value, ) class _ExplicitBucketHistogramAggregation(_Aggregation[HistogramPoint]): def __init__( self, attributes: Attributes, instrument_aggregation_temporality: AggregationTemporality, start_time_unix_nano: int, reservoir_builder: ExemplarReservoirBuilder, boundaries: Sequence[float] = ( 0.0, 5.0, 10.0, 25.0, 50.0, 75.0, 100.0, 250.0, 500.0, 750.0, 1000.0, 2500.0, 5000.0, 7500.0, 10000.0, ), record_min_max: bool = True, ): super().__init__( attributes, reservoir_builder=partial( reservoir_builder, boundaries=boundaries ), ) self._instrument_aggregation_temporality = ( instrument_aggregation_temporality ) self._start_time_unix_nano = start_time_unix_nano self._boundaries = tuple(boundaries) self._record_min_max = record_min_max self._value = None self._min = inf self._max = -inf self._sum = 0 self._previous_value = None self._previous_min = inf self._previous_max = -inf self._previous_sum = 0 self._previous_collection_start_nano = self._start_time_unix_nano def _get_empty_bucket_counts(self) -> List[int]: return [0] * (len(self._boundaries) + 1) def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ) -> None: with self._lock: if self._value is None: self._value = self._get_empty_bucket_counts() measurement_value = measurement.value self._sum += measurement_value if self._record_min_max: self._min = min(self._min, measurement_value) self._max = max(self._max, measurement_value) self._value[bisect_left(self._boundaries, measurement_value)] += 1 self._sample_exemplar(measurement, should_sample_exemplar) def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[_DataPointVarT]: """ Atomically return a point for the current value of the metric. """ with self._lock: value = self._value sum_ = self._sum min_ = self._min max_ = self._max self._value = None self._sum = 0 self._min = inf self._max = -inf if ( self._instrument_aggregation_temporality is AggregationTemporality.DELTA ): # This happens when the corresponding instrument for this # aggregation is synchronous. if ( collection_aggregation_temporality is AggregationTemporality.DELTA ): previous_collection_start_nano = ( self._previous_collection_start_nano ) self._previous_collection_start_nano = ( collection_start_nano ) if value is None: return None return HistogramDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=previous_collection_start_nano, time_unix_nano=collection_start_nano, count=sum(value), sum=sum_, bucket_counts=tuple(value), explicit_bounds=self._boundaries, min=min_, max=max_, ) if value is None: value = self._get_empty_bucket_counts() if self._previous_value is None: self._previous_value = self._get_empty_bucket_counts() self._previous_value = [ value_element + previous_value_element for ( value_element, previous_value_element, ) in zip(value, self._previous_value) ] self._previous_min = min(min_, self._previous_min) self._previous_max = max(max_, self._previous_max) self._previous_sum = sum_ + self._previous_sum return HistogramDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=self._start_time_unix_nano, time_unix_nano=collection_start_nano, count=sum(self._previous_value), sum=self._previous_sum, bucket_counts=tuple(self._previous_value), explicit_bounds=self._boundaries, min=self._previous_min, max=self._previous_max, ) return None # pylint: disable=protected-access class _ExponentialBucketHistogramAggregation(_Aggregation[HistogramPoint]): # _min_max_size and _max_max_size are the smallest and largest values # the max_size parameter may have, respectively. # _min_max_size is is the smallest reasonable value which is small enough # to contain the entire normal floating point range at the minimum scale. _min_max_size = 2 # _max_max_size is an arbitrary limit meant to limit accidental creation of # giant exponential bucket histograms. _max_max_size = 16384 def __init__( self, attributes: Attributes, reservoir_builder: ExemplarReservoirBuilder, instrument_aggregation_temporality: AggregationTemporality, start_time_unix_nano: int, # This is the default maximum number of buckets per positive or # negative number range. The value 160 is specified by OpenTelemetry. # See the derivation here: # https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exponential-bucket-histogram-aggregation) max_size: int = 160, max_scale: int = 20, ): # max_size is the maximum capacity of the positive and negative # buckets. # _sum is the sum of all the values aggregated by this aggregator. # _count is the count of all calls to aggregate. # _zero_count is the count of all the calls to aggregate when the value # to be aggregated is exactly 0. # _min is the smallest value aggregated by this aggregator. # _max is the smallest value aggregated by this aggregator. # _positive holds the positive values. # _negative holds the negative values by their absolute value. if max_size < self._min_max_size: raise ValueError( f"Buckets max size {max_size} is smaller than " "minimum max size {self._min_max_size}" ) if max_size > self._max_max_size: raise ValueError( f"Buckets max size {max_size} is larger than " "maximum max size {self._max_max_size}" ) if max_scale > 20: _logger.warning( "max_scale is set to %s which is " "larger than the recommended value of 20", max_scale, ) # This aggregation is analogous to _ExplicitBucketHistogramAggregation, # the only difference is that with every call to aggregate, the size # and amount of buckets can change (in # _ExplicitBucketHistogramAggregation both size and amount of buckets # remain constant once it is instantiated). super().__init__( attributes, reservoir_builder=partial( reservoir_builder, size=min(20, max_size) ), ) self._instrument_aggregation_temporality = ( instrument_aggregation_temporality ) self._start_time_unix_nano = start_time_unix_nano self._max_size = max_size self._max_scale = max_scale self._value_positive = None self._value_negative = None self._min = inf self._max = -inf self._sum = 0 self._count = 0 self._zero_count = 0 self._scale = None self._previous_value_positive = None self._previous_value_negative = None self._previous_min = inf self._previous_max = -inf self._previous_sum = 0 self._previous_count = 0 self._previous_zero_count = 0 self._previous_scale = None self._previous_collection_start_nano = self._start_time_unix_nano self._mapping = self._new_mapping(self._max_scale) def aggregate( self, measurement: Measurement, should_sample_exemplar: bool = True ) -> None: # pylint: disable=too-many-branches,too-many-statements, too-many-locals with self._lock: if self._value_positive is None: self._value_positive = Buckets() if self._value_negative is None: self._value_negative = Buckets() measurement_value = measurement.value self._sum += measurement_value self._min = min(self._min, measurement_value) self._max = max(self._max, measurement_value) self._count += 1 if measurement_value == 0: self._zero_count += 1 if self._count == self._zero_count: self._scale = 0 return if measurement_value > 0: value = self._value_positive else: measurement_value = -measurement_value value = self._value_negative # The following code finds out if it is necessary to change the # buckets to hold the incoming measurement_value, changes them if # necessary. This process does not exist in # _ExplicitBucketHistogram aggregation because the buckets there # are constant in size and amount. index = self._mapping.map_to_index(measurement_value) is_rescaling_needed = False low, high = 0, 0 if len(value) == 0: value.index_start = index value.index_end = index value.index_base = index elif ( index < value.index_start and (value.index_end - index) >= self._max_size ): is_rescaling_needed = True low = index high = value.index_end elif ( index > value.index_end and (index - value.index_start) >= self._max_size ): is_rescaling_needed = True low = value.index_start high = index if is_rescaling_needed: scale_change = self._get_scale_change(low, high) self._downscale( scale_change, self._value_positive, self._value_negative, ) self._mapping = self._new_mapping( self._mapping.scale - scale_change ) index = self._mapping.map_to_index(measurement_value) self._scale = self._mapping.scale if index < value.index_start: span = value.index_end - index if span >= len(value.counts): value.grow(span + 1, self._max_size) value.index_start = index elif index > value.index_end: span = index - value.index_start if span >= len(value.counts): value.grow(span + 1, self._max_size) value.index_end = index bucket_index = index - value.index_base if bucket_index < 0: bucket_index += len(value.counts) # Now the buckets have been changed if needed and bucket_index will # be used to increment the counter of the bucket that needs to be # incremented. # This is analogous to # self._value[bisect_left(self._boundaries, measurement_value)] += 1 # in _ExplicitBucketHistogramAggregation.aggregate value.increment_bucket(bucket_index) self._sample_exemplar(measurement, should_sample_exemplar) def collect( self, collection_aggregation_temporality: AggregationTemporality, collection_start_nano: int, ) -> Optional[_DataPointVarT]: """ Atomically return a point for the current value of the metric. """ # pylint: disable=too-many-statements, too-many-locals with self._lock: value_positive = self._value_positive value_negative = self._value_negative sum_ = self._sum min_ = self._min max_ = self._max count = self._count zero_count = self._zero_count scale = self._scale self._value_positive = None self._value_negative = None self._sum = 0 self._min = inf self._max = -inf self._count = 0 self._zero_count = 0 self._scale = None if ( self._instrument_aggregation_temporality is AggregationTemporality.DELTA ): # This happens when the corresponding instrument for this # aggregation is synchronous. if ( collection_aggregation_temporality is AggregationTemporality.DELTA ): previous_collection_start_nano = ( self._previous_collection_start_nano ) self._previous_collection_start_nano = ( collection_start_nano ) if value_positive is None and value_negative is None: return None return ExponentialHistogramDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=previous_collection_start_nano, time_unix_nano=collection_start_nano, count=count, sum=sum_, scale=scale, zero_count=zero_count, positive=BucketsPoint( offset=value_positive.offset, bucket_counts=(value_positive.get_offset_counts()), ), negative=BucketsPoint( offset=value_negative.offset, bucket_counts=(value_negative.get_offset_counts()), ), # FIXME: Find the right value for flags flags=0, min=min_, max=max_, ) # Here collection_temporality is CUMULATIVE. # instrument_temporality is always DELTA for the time being. # Here we need to handle the case where: # collect is called after at least one other call to collect # (there is data in previous buckets, a call to merge is needed # to handle possible differences in bucket sizes). # collect is called without another call previous call to # collect was made (there is no previous buckets, previous, # empty buckets that are the same scale of the current buckets # need to be made so that they can be cumulatively aggregated # to the current buckets). if ( value_positive is None and self._previous_value_positive is None ): # This happens if collect is called for the first time # and aggregate has not yet been called. value_positive = Buckets() self._previous_value_positive = value_positive.copy_empty() if ( value_negative is None and self._previous_value_negative is None ): value_negative = Buckets() self._previous_value_negative = value_negative.copy_empty() if scale is None and self._previous_scale is None: scale = self._mapping.scale self._previous_scale = scale if ( value_positive is not None and self._previous_value_positive is None ): # This happens when collect is called the very first time # and aggregate has been called before. # We need previous buckets to add them to the current ones. # When collect is called for the first time, there are no # previous buckets, so we need to create empty buckets to # add them to the current ones. The addition of empty # buckets to the current ones will result in the current # ones unchanged. # The way the previous buckets are generated here is # different from the explicit bucket histogram where # the size and amount of the buckets does not change once # they are instantiated. Here, the size and amount of the # buckets can change with every call to aggregate. In order # to get empty buckets that can be added to the current # ones resulting in the current ones unchanged we need to # generate empty buckets that have the same size and amount # as the current ones, this is what copy_empty does. self._previous_value_positive = value_positive.copy_empty() if ( value_negative is not None and self._previous_value_negative is None ): self._previous_value_negative = value_negative.copy_empty() if scale is not None and self._previous_scale is None: self._previous_scale = scale if ( value_positive is None and self._previous_value_positive is not None ): value_positive = self._previous_value_positive.copy_empty() if ( value_negative is None and self._previous_value_negative is not None ): value_negative = self._previous_value_negative.copy_empty() if scale is None and self._previous_scale is not None: scale = self._previous_scale min_scale = min(self._previous_scale, scale) low_positive, high_positive = ( self._get_low_high_previous_current( self._previous_value_positive, value_positive, scale, min_scale, ) ) low_negative, high_negative = ( self._get_low_high_previous_current( self._previous_value_negative, value_negative, scale, min_scale, ) ) min_scale = min( min_scale - self._get_scale_change(low_positive, high_positive), min_scale - self._get_scale_change(low_negative, high_negative), ) self._downscale( self._previous_scale - min_scale, self._previous_value_positive, self._previous_value_negative, ) # self._merge adds the values from value to # self._previous_value, this is analogous to # self._previous_value = [ # value_element + previous_value_element # for ( # value_element, # previous_value_element, # ) in zip(value, self._previous_value) # ] # in _ExplicitBucketHistogramAggregation.collect. self._merge( self._previous_value_positive, value_positive, scale, min_scale, collection_aggregation_temporality, ) self._merge( self._previous_value_negative, value_negative, scale, min_scale, collection_aggregation_temporality, ) self._previous_min = min(min_, self._previous_min) self._previous_max = max(max_, self._previous_max) self._previous_sum = sum_ + self._previous_sum self._previous_count = count + self._previous_count self._previous_zero_count = ( zero_count + self._previous_zero_count ) self._previous_scale = min_scale return ExponentialHistogramDataPoint( attributes=self._attributes, exemplars=self._collect_exemplars(), start_time_unix_nano=self._start_time_unix_nano, time_unix_nano=collection_start_nano, count=self._previous_count, sum=self._previous_sum, scale=self._previous_scale, zero_count=self._previous_zero_count, positive=BucketsPoint( offset=self._previous_value_positive.offset, bucket_counts=( self._previous_value_positive.get_offset_counts() ), ), negative=BucketsPoint( offset=self._previous_value_negative.offset, bucket_counts=( self._previous_value_negative.get_offset_counts() ), ), # FIXME: Find the right value for flags flags=0, min=self._previous_min, max=self._previous_max, ) return None def _get_low_high_previous_current( self, previous_point_buckets, current_point_buckets, current_scale, min_scale, ): (previous_point_low, previous_point_high) = self._get_low_high( previous_point_buckets, self._previous_scale, min_scale ) (current_point_low, current_point_high) = self._get_low_high( current_point_buckets, current_scale, min_scale ) if current_point_low > current_point_high: low = previous_point_low high = previous_point_high elif previous_point_low > previous_point_high: low = current_point_low high = current_point_high else: low = min(previous_point_low, current_point_low) high = max(previous_point_high, current_point_high) return low, high @staticmethod def _get_low_high(buckets, scale, min_scale): if buckets.counts == [0]: return 0, -1 shift = scale - min_scale return buckets.index_start >> shift, buckets.index_end >> shift @staticmethod def _new_mapping(scale: int) -> Mapping: if scale <= 0: return ExponentMapping(scale) return LogarithmMapping(scale) def _get_scale_change(self, low, high): change = 0 while high - low >= self._max_size: high = high >> 1 low = low >> 1 change += 1 return change @staticmethod def _downscale(change: int, positive, negative): if change == 0: return if change < 0: # pylint: disable=broad-exception-raised raise Exception("Invalid change of scale") positive.downscale(change) negative.downscale(change) def _merge( self, previous_buckets: Buckets, current_buckets: Buckets, current_scale, min_scale, aggregation_temporality, ): current_change = current_scale - min_scale for current_bucket_index, current_bucket in enumerate( current_buckets.counts ): if current_bucket == 0: continue # Not considering the case where len(previous_buckets) == 0. This # would not happen because self._previous_point is only assigned to # an ExponentialHistogramDataPoint object if self._count != 0. current_index = current_buckets.index_base + current_bucket_index if current_index > current_buckets.index_end: current_index -= len(current_buckets.counts) index = current_index >> current_change if index < previous_buckets.index_start: span = previous_buckets.index_end - index if span >= self._max_size: # pylint: disable=broad-exception-raised raise Exception("Incorrect merge scale") if span >= len(previous_buckets.counts): previous_buckets.grow(span + 1, self._max_size) previous_buckets.index_start = index if index > previous_buckets.index_end: span = index - previous_buckets.index_start if span >= self._max_size: # pylint: disable=broad-exception-raised raise Exception("Incorrect merge scale") if span >= len(previous_buckets.counts): previous_buckets.grow(span + 1, self._max_size) previous_buckets.index_end = index bucket_index = index - previous_buckets.index_base if bucket_index < 0: bucket_index += len(previous_buckets.counts) if aggregation_temporality is AggregationTemporality.DELTA: current_bucket = -current_bucket previous_buckets.increment_bucket( bucket_index, increment=current_bucket ) class Aggregation(ABC): """ Base class for all aggregation types. """ @abstractmethod def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: """Creates an aggregation""" class DefaultAggregation(Aggregation): """ The default aggregation to be used in a `View`. This aggregation will create an actual aggregation depending on the instrument type, as specified next: ==================================================== ==================================== Instrument Aggregation ==================================================== ==================================== `opentelemetry.sdk.metrics.Counter` `SumAggregation` `opentelemetry.sdk.metrics.UpDownCounter` `SumAggregation` `opentelemetry.sdk.metrics.ObservableCounter` `SumAggregation` `opentelemetry.sdk.metrics.ObservableUpDownCounter` `SumAggregation` `opentelemetry.sdk.metrics.Histogram` `ExplicitBucketHistogramAggregation` `opentelemetry.sdk.metrics.ObservableGauge` `LastValueAggregation` ==================================================== ==================================== """ def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: # pylint: disable=too-many-return-statements if isinstance(instrument, Counter): return _SumAggregation( attributes, reservoir_builder=reservoir_factory(_SumAggregation), instrument_is_monotonic=True, instrument_aggregation_temporality=( AggregationTemporality.DELTA ), start_time_unix_nano=start_time_unix_nano, ) if isinstance(instrument, UpDownCounter): return _SumAggregation( attributes, reservoir_builder=reservoir_factory(_SumAggregation), instrument_is_monotonic=False, instrument_aggregation_temporality=( AggregationTemporality.DELTA ), start_time_unix_nano=start_time_unix_nano, ) if isinstance(instrument, ObservableCounter): return _SumAggregation( attributes, reservoir_builder=reservoir_factory(_SumAggregation), instrument_is_monotonic=True, instrument_aggregation_temporality=( AggregationTemporality.CUMULATIVE ), start_time_unix_nano=start_time_unix_nano, ) if isinstance(instrument, ObservableUpDownCounter): return _SumAggregation( attributes, reservoir_builder=reservoir_factory(_SumAggregation), instrument_is_monotonic=False, instrument_aggregation_temporality=( AggregationTemporality.CUMULATIVE ), start_time_unix_nano=start_time_unix_nano, ) if isinstance(instrument, Histogram): return _ExplicitBucketHistogramAggregation( attributes, reservoir_builder=reservoir_factory( _ExplicitBucketHistogramAggregation ), instrument_aggregation_temporality=( AggregationTemporality.DELTA ), start_time_unix_nano=start_time_unix_nano, ) if isinstance(instrument, ObservableGauge): return _LastValueAggregation( attributes, reservoir_builder=reservoir_factory(_LastValueAggregation), ) if isinstance(instrument, _Gauge): return _LastValueAggregation( attributes, reservoir_builder=reservoir_factory(_LastValueAggregation), ) # pylint: disable=broad-exception-raised raise Exception(f"Invalid instrument type {type(instrument)} found") class ExponentialBucketHistogramAggregation(Aggregation): def __init__( self, max_size: int = 160, max_scale: int = 20, ): self._max_size = max_size self._max_scale = max_scale def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED if isinstance(instrument, Synchronous): instrument_aggregation_temporality = AggregationTemporality.DELTA elif isinstance(instrument, Asynchronous): instrument_aggregation_temporality = ( AggregationTemporality.CUMULATIVE ) return _ExponentialBucketHistogramAggregation( attributes, reservoir_factory(_ExponentialBucketHistogramAggregation), instrument_aggregation_temporality, start_time_unix_nano, max_size=self._max_size, max_scale=self._max_scale, ) class ExplicitBucketHistogramAggregation(Aggregation): """This aggregation informs the SDK to collect: - Count of Measurement values falling within explicit bucket boundaries. - Arithmetic sum of Measurement values in population. This SHOULD NOT be collected when used with instruments that record negative measurements, e.g. UpDownCounter or ObservableGauge. - Min (optional) Measurement value in population. - Max (optional) Measurement value in population. Args: boundaries: Array of increasing values representing explicit bucket boundary values. record_min_max: Whether to record min and max. """ def __init__( self, boundaries: Sequence[float] = ( 0.0, 5.0, 10.0, 25.0, 50.0, 75.0, 100.0, 250.0, 500.0, 750.0, 1000.0, 2500.0, 5000.0, 7500.0, 10000.0, ), record_min_max: bool = True, ) -> None: self._boundaries = boundaries self._record_min_max = record_min_max def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED if isinstance(instrument, Synchronous): instrument_aggregation_temporality = AggregationTemporality.DELTA elif isinstance(instrument, Asynchronous): instrument_aggregation_temporality = ( AggregationTemporality.CUMULATIVE ) return _ExplicitBucketHistogramAggregation( attributes, instrument_aggregation_temporality, start_time_unix_nano, reservoir_factory(_ExplicitBucketHistogramAggregation), self._boundaries, self._record_min_max, ) class SumAggregation(Aggregation): """This aggregation informs the SDK to collect: - The arithmetic sum of Measurement values. """ def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED if isinstance(instrument, Synchronous): instrument_aggregation_temporality = AggregationTemporality.DELTA elif isinstance(instrument, Asynchronous): instrument_aggregation_temporality = ( AggregationTemporality.CUMULATIVE ) return _SumAggregation( attributes, isinstance(instrument, (Counter, ObservableCounter)), instrument_aggregation_temporality, start_time_unix_nano, reservoir_factory(_SumAggregation), ) class LastValueAggregation(Aggregation): """ This aggregation informs the SDK to collect: - The last Measurement. - The timestamp of the last Measurement. """ def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: return _LastValueAggregation( attributes, reservoir_builder=reservoir_factory(_LastValueAggregation), ) class DropAggregation(Aggregation): """Using this aggregation will make all measurements be ignored.""" def _create_aggregation( self, instrument: Instrument, attributes: Attributes, reservoir_factory: Callable[ [Type[_Aggregation]], ExemplarReservoirBuilder ], start_time_unix_nano: int, ) -> _Aggregation: return _DropAggregation( attributes, reservoir_factory(_DropAggregation) )