/
sum.go
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/
sum.go
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// 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.
package internal // import "go.opentelemetry.io/otel/sdk/metric/internal"
import (
"sync"
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
// valueMap is the storage for sums.
type valueMap[N int64 | float64] struct {
sync.Mutex
values map[attribute.Set]N
}
func newValueMap[N int64 | float64]() *valueMap[N] {
return &valueMap[N]{values: make(map[attribute.Set]N)}
}
func (s *valueMap[N]) Aggregate(value N, attr attribute.Set) {
s.Lock()
s.values[attr] += value
s.Unlock()
}
// NewDeltaSum returns an Aggregator that summarizes a set of measurements as
// their arithmetic sum. Each sum is scoped by attributes and the aggregation
// cycle the measurements were made in.
//
// The monotonic value is used to communicate the produced Aggregation is
// monotonic or not. The returned Aggregator does not make any guarantees this
// value is accurate. It is up to the caller to ensure it.
//
// Each aggregation cycle is treated independently. When the returned
// Aggregator's Aggregation method is called it will reset all sums to zero.
func NewDeltaSum[N int64 | float64](monotonic bool) Aggregator[N] {
return newDeltaSum[N](monotonic)
}
func newDeltaSum[N int64 | float64](monotonic bool) *deltaSum[N] {
return &deltaSum[N]{
valueMap: newValueMap[N](),
monotonic: monotonic,
start: now(),
}
}
// deltaSum summarizes a set of measurements made in a single aggregation
// cycle as their arithmetic sum.
type deltaSum[N int64 | float64] struct {
*valueMap[N]
monotonic bool
start time.Time
}
func (s *deltaSum[N]) Aggregation() metricdata.Aggregation {
s.Lock()
defer s.Unlock()
if len(s.values) == 0 {
return nil
}
t := now()
out := metricdata.Sum[N]{
Temporality: metricdata.DeltaTemporality,
IsMonotonic: s.monotonic,
DataPoints: make([]metricdata.DataPoint[N], 0, len(s.values)),
}
for attr, value := range s.values {
out.DataPoints = append(out.DataPoints, metricdata.DataPoint[N]{
Attributes: attr,
StartTime: s.start,
Time: t,
Value: value,
})
// Unused attribute sets do not report.
delete(s.values, attr)
}
// The delta collection cycle resets.
s.start = t
return out
}
// NewCumulativeSum returns an Aggregator that summarizes a set of
// measurements as their arithmetic sum. Each sum is scoped by attributes and
// the aggregation cycle the measurements were made in.
//
// The monotonic value is used to communicate the produced Aggregation is
// monotonic or not. The returned Aggregator does not make any guarantees this
// value is accurate. It is up to the caller to ensure it.
//
// Each aggregation cycle is treated independently. When the returned
// Aggregator's Aggregation method is called it will reset all sums to zero.
func NewCumulativeSum[N int64 | float64](monotonic bool) Aggregator[N] {
return newCumulativeSum[N](monotonic)
}
func newCumulativeSum[N int64 | float64](monotonic bool) *cumulativeSum[N] {
return &cumulativeSum[N]{
valueMap: newValueMap[N](),
monotonic: monotonic,
start: now(),
}
}
// cumulativeSum summarizes a set of measurements made over all aggregation
// cycles as their arithmetic sum.
type cumulativeSum[N int64 | float64] struct {
*valueMap[N]
monotonic bool
start time.Time
}
func (s *cumulativeSum[N]) Aggregation() metricdata.Aggregation {
s.Lock()
defer s.Unlock()
if len(s.values) == 0 {
return nil
}
t := now()
out := metricdata.Sum[N]{
Temporality: metricdata.CumulativeTemporality,
IsMonotonic: s.monotonic,
DataPoints: make([]metricdata.DataPoint[N], 0, len(s.values)),
}
for attr, value := range s.values {
out.DataPoints = append(out.DataPoints, metricdata.DataPoint[N]{
Attributes: attr,
StartTime: s.start,
Time: t,
Value: value,
})
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute
// sets that become "stale" need to be forgotten so this will not
// overload the system.
}
return out
}
// precomputedValue is the recorded measurement value for a set of attributes.
type precomputedValue[N int64 | float64] struct {
// measured is the last value measured for a set of attributes that were
// not filtered.
measured N
// filtered is the sum of values from measurements that had their
// attributes filtered.
filtered N
}
// precomputedMap is the storage for precomputed sums.
type precomputedMap[N int64 | float64] struct {
sync.Mutex
values map[attribute.Set]precomputedValue[N]
}
func newPrecomputedMap[N int64 | float64]() *precomputedMap[N] {
return &precomputedMap[N]{
values: make(map[attribute.Set]precomputedValue[N]),
}
}
// Aggregate records value with the unfiltered attributes attr.
//
// If a previous measurement was made for the same attribute set:
//
// - If that measurement's attributes were not filtered, this value overwrite
// that value.
// - If that measurement's attributes were filtered, this value will be
// recorded along side that value.
func (s *precomputedMap[N]) Aggregate(value N, attr attribute.Set) {
s.Lock()
v := s.values[attr]
v.measured = value
s.values[attr] = v
s.Unlock()
}
// aggregateFiltered records value with the filtered attributes attr.
//
// If a previous measurement was made for the same attribute set:
//
// - If that measurement's attributes were not filtered, this value will be
// recorded along side that value.
// - If that measurement's attributes were filtered, this value will be
// added to it.
//
// This method should not be used if attr have not been reduced by an attribute
// filter.
func (s *precomputedMap[N]) aggregateFiltered(value N, attr attribute.Set) { // nolint: unused // Used to agg filtered.
s.Lock()
v := s.values[attr]
v.filtered += value
s.values[attr] = v
s.Unlock()
}
// NewPrecomputedDeltaSum returns an Aggregator that summarizes a set of
// pre-computed sums. Each sum is scoped by attributes and the aggregation
// cycle the measurements were made in.
//
// The monotonic value is used to communicate the produced Aggregation is
// monotonic or not. The returned Aggregator does not make any guarantees this
// value is accurate. It is up to the caller to ensure it.
//
// The output Aggregation will report recorded values as delta temporality.
func NewPrecomputedDeltaSum[N int64 | float64](monotonic bool) Aggregator[N] {
return &precomputedDeltaSum[N]{
precomputedMap: newPrecomputedMap[N](),
reported: make(map[attribute.Set]N),
monotonic: monotonic,
start: now(),
}
}
// precomputedDeltaSum summarizes a set of pre-computed sums recorded over all
// aggregation cycles as the delta of these sums.
type precomputedDeltaSum[N int64 | float64] struct {
*precomputedMap[N]
reported map[attribute.Set]N
monotonic bool
start time.Time
}
// Aggregation returns the recorded pre-computed sums as an Aggregation. The
// sum values are expressed as the delta between what was measured this
// collection cycle and the previous.
//
// All pre-computed sums that were recorded for attributes sets reduced by an
// attribute filter (filtered-sums) are summed together and added to any
// pre-computed sum value recorded directly for the resulting attribute set
// (unfiltered-sum). The filtered-sums are reset to zero for the next
// collection cycle, and the unfiltered-sum is kept for the next collection
// cycle.
func (s *precomputedDeltaSum[N]) Aggregation() metricdata.Aggregation {
s.Lock()
defer s.Unlock()
if len(s.values) == 0 {
return nil
}
t := now()
out := metricdata.Sum[N]{
Temporality: metricdata.DeltaTemporality,
IsMonotonic: s.monotonic,
DataPoints: make([]metricdata.DataPoint[N], 0, len(s.values)),
}
for attr, value := range s.values {
v := value.measured + value.filtered
delta := v - s.reported[attr]
out.DataPoints = append(out.DataPoints, metricdata.DataPoint[N]{
Attributes: attr,
StartTime: s.start,
Time: t,
Value: delta,
})
if delta != 0 {
s.reported[attr] = v
}
value.filtered = N(0)
s.values[attr] = value
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute
// sets that become "stale" need to be forgotten so this will not
// overload the system.
}
// The delta collection cycle resets.
s.start = t
return out
}
// NewPrecomputedCumulativeSum returns an Aggregator that summarizes a set of
// pre-computed sums. Each sum is scoped by attributes and the aggregation
// cycle the measurements were made in.
//
// The monotonic value is used to communicate the produced Aggregation is
// monotonic or not. The returned Aggregator does not make any guarantees this
// value is accurate. It is up to the caller to ensure it.
//
// The output Aggregation will report recorded values as cumulative
// temporality.
func NewPrecomputedCumulativeSum[N int64 | float64](monotonic bool) Aggregator[N] {
return &precomputedCumulativeSum[N]{
precomputedMap: newPrecomputedMap[N](),
monotonic: monotonic,
start: now(),
}
}
// precomputedCumulativeSum directly records and reports a set of pre-computed sums.
type precomputedCumulativeSum[N int64 | float64] struct {
*precomputedMap[N]
monotonic bool
start time.Time
}
// Aggregation returns the recorded pre-computed sums as an Aggregation. The
// sum values are expressed directly as they are assumed to be recorded as the
// cumulative sum of a some measured phenomena.
//
// All pre-computed sums that were recorded for attributes sets reduced by an
// attribute filter (filtered-sums) are summed together and added to any
// pre-computed sum value recorded directly for the resulting attribute set
// (unfiltered-sum). The filtered-sums are reset to zero for the next
// collection cycle, and the unfiltered-sum is kept for the next collection
// cycle.
func (s *precomputedCumulativeSum[N]) Aggregation() metricdata.Aggregation {
s.Lock()
defer s.Unlock()
if len(s.values) == 0 {
return nil
}
t := now()
out := metricdata.Sum[N]{
Temporality: metricdata.CumulativeTemporality,
IsMonotonic: s.monotonic,
DataPoints: make([]metricdata.DataPoint[N], 0, len(s.values)),
}
for attr, value := range s.values {
out.DataPoints = append(out.DataPoints, metricdata.DataPoint[N]{
Attributes: attr,
StartTime: s.start,
Time: t,
Value: value.measured + value.filtered,
})
value.filtered = N(0)
s.values[attr] = value
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute
// sets that become "stale" need to be forgotten so this will not
// overload the system.
}
return out
}