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opentelemetry-go/sdk/metric/aggregator/histogram/histogram.go

185 lines
5.6 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 histogram // import "go.opentelemetry.io/otel/sdk/metric/aggregator/histogram"
import (
"context"
"sort"
"sync"
"go.opentelemetry.io/otel/api/metric"
export "go.opentelemetry.io/otel/sdk/export/metric"
"go.opentelemetry.io/otel/sdk/export/metric/aggregation"
"go.opentelemetry.io/otel/sdk/metric/aggregator"
)
// Note: This code uses a Mutex to govern access to the exclusive
// aggregator state. This is in contrast to a lock-free approach
// (as in the Go prometheus client) that was reverted here:
// https://github.com/open-telemetry/opentelemetry-go/pull/669
type (
// Aggregator observe events and counts them in pre-determined buckets.
// It also calculates the sum and count of all events.
Aggregator struct {
lock sync.Mutex
boundaries []float64
kind metric.NumberKind
state state
}
// state represents the state of a histogram, consisting of
// the sum and counts for all observed values and
// the less than equal bucket count for the pre-determined boundaries.
state struct {
bucketCounts []float64
count metric.Number
sum metric.Number
}
)
var _ export.Aggregator = &Aggregator{}
var _ aggregation.Sum = &Aggregator{}
var _ aggregation.Count = &Aggregator{}
var _ aggregation.Histogram = &Aggregator{}
// New returns a new aggregator for computing Histograms.
//
// A Histogram observe events and counts them in pre-defined buckets.
// And also provides the total sum and count of all observations.
//
// Note that this aggregator maintains each value using independent
// atomic operations, which introduces the possibility that
// checkpoints are inconsistent.
func New(cnt int, desc *metric.Descriptor, boundaries []float64) []Aggregator {
aggs := make([]Aggregator, cnt)
// Boundaries MUST be ordered otherwise the histogram could not
// be properly computed.
sortedBoundaries := make([]float64, len(boundaries))
copy(sortedBoundaries, boundaries)
sort.Float64s(sortedBoundaries)
for i := range aggs {
aggs[i] = Aggregator{
kind: desc.NumberKind(),
boundaries: sortedBoundaries,
state: emptyState(sortedBoundaries),
}
}
return aggs
}
// Aggregation returns an interface for reading the state of this aggregator.
func (c *Aggregator) Aggregation() aggregation.Aggregation {
return c
}
// Kind returns aggregation.HistogramKind.
func (c *Aggregator) Kind() aggregation.Kind {
return aggregation.HistogramKind
}
// Sum returns the sum of all values in the checkpoint.
func (c *Aggregator) Sum() (metric.Number, error) {
return c.state.sum, nil
}
// Count returns the number of values in the checkpoint.
func (c *Aggregator) Count() (int64, error) {
return int64(c.state.count), nil
}
// Histogram returns the count of events in pre-determined buckets.
func (c *Aggregator) Histogram() (aggregation.Buckets, error) {
return aggregation.Buckets{
Boundaries: c.boundaries,
Counts: c.state.bucketCounts,
}, nil
}
// SynchronizedMove saves the current state into oa and resets the current state to
// the empty set. Since no locks are taken, there is a chance that
// the independent Sum, Count and Bucket Count are not consistent with each
// other.
func (c *Aggregator) SynchronizedMove(oa export.Aggregator, desc *metric.Descriptor) error {
o, _ := oa.(*Aggregator)
if o == nil {
return aggregator.NewInconsistentAggregatorError(c, oa)
}
c.lock.Lock()
o.state, c.state = c.state, emptyState(c.boundaries)
c.lock.Unlock()
return nil
}
func emptyState(boundaries []float64) state {
return state{
bucketCounts: make([]float64, len(boundaries)+1),
}
}
// Update adds the recorded measurement to the current data set.
func (c *Aggregator) Update(_ context.Context, number metric.Number, desc *metric.Descriptor) error {
kind := desc.NumberKind()
asFloat := number.CoerceToFloat64(kind)
bucketID := len(c.boundaries)
for i, boundary := range c.boundaries {
if asFloat < boundary {
bucketID = i
break
}
}
// Note: Binary-search was compared using the benchmarks. The following
// code is equivalent to the linear search above:
//
// bucketID := sort.Search(len(c.boundaries), func(i int) bool {
// return asFloat < c.boundaries[i]
// })
//
// The binary search wins for very large boundary sets, but
// the linear search performs better up through arrays between
// 256 and 512 elements, which is a relatively large histogram, so we
// continue to prefer linear search.
c.lock.Lock()
defer c.lock.Unlock()
c.state.count.AddInt64(1)
c.state.sum.AddNumber(kind, number)
c.state.bucketCounts[bucketID]++
return nil
}
// Merge combines two histograms that have the same buckets into a single one.
func (c *Aggregator) Merge(oa export.Aggregator, desc *metric.Descriptor) error {
o, _ := oa.(*Aggregator)
if o == nil {
return aggregator.NewInconsistentAggregatorError(c, oa)
}
c.state.sum.AddNumber(desc.NumberKind(), o.state.sum)
c.state.count.AddNumber(metric.Uint64NumberKind, o.state.count)
for i := 0; i < len(c.state.bucketCounts); i++ {
c.state.bucketCounts[i] += o.state.bucketCounts[i]
}
return nil
}