forked from TrueCloudLab/distribution
0e3018f2cf
Signed-off-by: gotgelf <gotgelf@gmail.com>
231 lines
6.1 KiB
Go
231 lines
6.1 KiB
Go
// 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"
|
|
"sort"
|
|
"sync"
|
|
"time"
|
|
|
|
"go.opentelemetry.io/otel/attribute"
|
|
"go.opentelemetry.io/otel/sdk/metric/metricdata"
|
|
)
|
|
|
|
type buckets[N int64 | float64] struct {
|
|
counts []uint64
|
|
count uint64
|
|
total N
|
|
min, max N
|
|
}
|
|
|
|
// newBuckets returns buckets with n bins.
|
|
func newBuckets[N int64 | float64](n int) *buckets[N] {
|
|
return &buckets[N]{counts: make([]uint64, n)}
|
|
}
|
|
|
|
func (b *buckets[N]) sum(value N) { b.total += value }
|
|
|
|
func (b *buckets[N]) bin(idx int, value N) {
|
|
b.counts[idx]++
|
|
b.count++
|
|
if value < b.min {
|
|
b.min = value
|
|
} else if value > b.max {
|
|
b.max = value
|
|
}
|
|
}
|
|
|
|
// histValues summarizes a set of measurements as an histValues with
|
|
// explicitly defined buckets.
|
|
type histValues[N int64 | float64] struct {
|
|
noSum bool
|
|
bounds []float64
|
|
|
|
values map[attribute.Set]*buckets[N]
|
|
valuesMu sync.Mutex
|
|
}
|
|
|
|
func newHistValues[N int64 | float64](bounds []float64, noSum bool) *histValues[N] {
|
|
// The responsibility of keeping all buckets correctly associated with the
|
|
// passed boundaries is ultimately this type's responsibility. Make a copy
|
|
// here so we can always guarantee this. Or, in the case of failure, have
|
|
// complete control over the fix.
|
|
b := make([]float64, len(bounds))
|
|
copy(b, bounds)
|
|
sort.Float64s(b)
|
|
return &histValues[N]{
|
|
noSum: noSum,
|
|
bounds: b,
|
|
values: make(map[attribute.Set]*buckets[N]),
|
|
}
|
|
}
|
|
|
|
// Aggregate records the measurement value, scoped by attr, and aggregates it
|
|
// into a histogram.
|
|
func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set) {
|
|
// This search will return an index in the range [0, len(s.bounds)], where
|
|
// it will return len(s.bounds) if value is greater than the last element
|
|
// of s.bounds. This aligns with the buckets in that the length of buckets
|
|
// is len(s.bounds)+1, with the last bucket representing:
|
|
// (s.bounds[len(s.bounds)-1], +∞).
|
|
idx := sort.SearchFloat64s(s.bounds, float64(value))
|
|
|
|
s.valuesMu.Lock()
|
|
defer s.valuesMu.Unlock()
|
|
|
|
b, ok := s.values[attr]
|
|
if !ok {
|
|
// N+1 buckets. For example:
|
|
//
|
|
// bounds = [0, 5, 10]
|
|
//
|
|
// Then,
|
|
//
|
|
// buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
|
|
b = newBuckets[N](len(s.bounds) + 1)
|
|
// Ensure min and max are recorded values (not zero), for new buckets.
|
|
b.min, b.max = value, value
|
|
s.values[attr] = b
|
|
}
|
|
b.bin(idx, value)
|
|
if !s.noSum {
|
|
b.sum(value)
|
|
}
|
|
}
|
|
|
|
// newHistogram returns an Aggregator that summarizes a set of measurements as
|
|
// an histogram.
|
|
func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool) *histogram[N] {
|
|
return &histogram[N]{
|
|
histValues: newHistValues[N](boundaries, noSum),
|
|
noMinMax: noMinMax,
|
|
start: now(),
|
|
}
|
|
}
|
|
|
|
// histogram summarizes a set of measurements as an histogram with explicitly
|
|
// defined buckets.
|
|
type histogram[N int64 | float64] struct {
|
|
*histValues[N]
|
|
|
|
noMinMax bool
|
|
start time.Time
|
|
}
|
|
|
|
func (s *histogram[N]) delta(dest *metricdata.Aggregation) int {
|
|
t := now()
|
|
|
|
// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
|
|
// case, use the zero-value h and hope for better alignment next cycle.
|
|
h, _ := (*dest).(metricdata.Histogram[N])
|
|
h.Temporality = metricdata.DeltaTemporality
|
|
|
|
s.valuesMu.Lock()
|
|
defer s.valuesMu.Unlock()
|
|
|
|
// Do not allow modification of our copy of bounds.
|
|
bounds := make([]float64, len(s.bounds))
|
|
copy(bounds, s.bounds)
|
|
|
|
n := len(s.values)
|
|
hDPts := reset(h.DataPoints, n, n)
|
|
|
|
var i int
|
|
for a, b := range s.values {
|
|
hDPts[i].Attributes = a
|
|
hDPts[i].StartTime = s.start
|
|
hDPts[i].Time = t
|
|
hDPts[i].Count = b.count
|
|
hDPts[i].Bounds = bounds
|
|
hDPts[i].BucketCounts = b.counts
|
|
|
|
if !s.noSum {
|
|
hDPts[i].Sum = b.total
|
|
}
|
|
|
|
if !s.noMinMax {
|
|
hDPts[i].Min = metricdata.NewExtrema(b.min)
|
|
hDPts[i].Max = metricdata.NewExtrema(b.max)
|
|
}
|
|
|
|
// Unused attribute sets do not report.
|
|
delete(s.values, a)
|
|
i++
|
|
}
|
|
// The delta collection cycle resets.
|
|
s.start = t
|
|
|
|
h.DataPoints = hDPts
|
|
*dest = h
|
|
|
|
return n
|
|
}
|
|
|
|
func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int {
|
|
t := now()
|
|
|
|
// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
|
|
// case, use the zero-value h and hope for better alignment next cycle.
|
|
h, _ := (*dest).(metricdata.Histogram[N])
|
|
h.Temporality = metricdata.CumulativeTemporality
|
|
|
|
s.valuesMu.Lock()
|
|
defer s.valuesMu.Unlock()
|
|
|
|
// Do not allow modification of our copy of bounds.
|
|
bounds := make([]float64, len(s.bounds))
|
|
copy(bounds, s.bounds)
|
|
|
|
n := len(s.values)
|
|
hDPts := reset(h.DataPoints, n, n)
|
|
|
|
var i int
|
|
for a, b := range s.values {
|
|
// The HistogramDataPoint field values returned need to be copies of
|
|
// the buckets value as we will keep updating them.
|
|
//
|
|
// TODO (#3047): Making copies for bounds and counts incurs a large
|
|
// memory allocation footprint. Alternatives should be explored.
|
|
counts := make([]uint64, len(b.counts))
|
|
copy(counts, b.counts)
|
|
|
|
hDPts[i].Attributes = a
|
|
hDPts[i].StartTime = s.start
|
|
hDPts[i].Time = t
|
|
hDPts[i].Count = b.count
|
|
hDPts[i].Bounds = bounds
|
|
hDPts[i].BucketCounts = counts
|
|
|
|
if !s.noSum {
|
|
hDPts[i].Sum = b.total
|
|
}
|
|
|
|
if !s.noMinMax {
|
|
hDPts[i].Min = metricdata.NewExtrema(b.min)
|
|
hDPts[i].Max = metricdata.NewExtrema(b.max)
|
|
}
|
|
i++
|
|
// 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.
|
|
}
|
|
|
|
h.DataPoints = hDPts
|
|
*dest = h
|
|
|
|
return n
|
|
}
|