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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 aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import (
"context"
"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]) measure(_ context.Context, value N, attr attribute.Set) {
s.Lock()
s.values[attr] += value
s.Unlock()
}
// newSum 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.
func newSum[N int64 | float64](monotonic bool) *sum[N] {
return &sum[N]{
valueMap: newValueMap[N](),
monotonic: monotonic,
start: now(),
}
}
// sum summarizes a set of measurements made as their arithmetic sum.
type sum[N int64 | float64] struct {
*valueMap[N]
monotonic bool
start time.Time
}
func (s *sum[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
// use the zero-value sData and hope for better alignment next cycle.
sData, _ := (*dest).(metricdata.Sum[N])
sData.Temporality = metricdata.DeltaTemporality
sData.IsMonotonic = s.monotonic
s.Lock()
defer s.Unlock()
n := len(s.values)
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = value
// Do not report stale values.
delete(s.values, attr)
i++
}
// The delta collection cycle resets.
s.start = t
sData.DataPoints = dPts
*dest = sData
return n
}
func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
// use the zero-value sData and hope for better alignment next cycle.
sData, _ := (*dest).(metricdata.Sum[N])
sData.Temporality = metricdata.CumulativeTemporality
sData.IsMonotonic = s.monotonic
s.Lock()
defer s.Unlock()
n := len(s.values)
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].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.
i++
}
sData.DataPoints = dPts
*dest = sData
return n
}
// newPrecomputedSum returns an aggregator that summarizes a set of
// observatrions as their arithmetic sum. Each sum is scoped by attributes and
// the aggregation cycle the measurements were made in.
func newPrecomputedSum[N int64 | float64](monotonic bool) *precomputedSum[N] {
return &precomputedSum[N]{
valueMap: newValueMap[N](),
monotonic: monotonic,
start: now(),
}
}
// precomputedSum summarizes a set of observatrions as their arithmetic sum.
type precomputedSum[N int64 | float64] struct {
*valueMap[N]
monotonic bool
start time.Time
reported map[attribute.Set]N
}
func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
t := now()
newReported := make(map[attribute.Set]N)
// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
// use the zero-value sData and hope for better alignment next cycle.
sData, _ := (*dest).(metricdata.Sum[N])
sData.Temporality = metricdata.DeltaTemporality
sData.IsMonotonic = s.monotonic
s.Lock()
defer s.Unlock()
n := len(s.values)
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
delta := value - s.reported[attr]
dPts[i].Attributes = attr
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = delta
newReported[attr] = value
// Unused attribute sets do not report.
delete(s.values, attr)
i++
}
// Unused attribute sets are forgotten.
s.reported = newReported
// The delta collection cycle resets.
s.start = t
sData.DataPoints = dPts
*dest = sData
return n
}
func (s *precomputedSum[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
// use the zero-value sData and hope for better alignment next cycle.
sData, _ := (*dest).(metricdata.Sum[N])
sData.Temporality = metricdata.CumulativeTemporality
sData.IsMonotonic = s.monotonic
s.Lock()
defer s.Unlock()
n := len(s.values)
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = value
// Unused attribute sets do not report.
delete(s.values, attr)
i++
}
sData.DataPoints = dPts
*dest = sData
return n
}
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