mirror of
https://github.com/open-telemetry/opentelemetry-go.git
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185 lines
5.6 KiB
Go
185 lines
5.6 KiB
Go
// Copyright The OpenTelemetry Authors
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package histogram // import "go.opentelemetry.io/otel/sdk/metric/aggregator/histogram"
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import (
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"context"
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"sort"
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"sync"
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"go.opentelemetry.io/otel/api/metric"
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export "go.opentelemetry.io/otel/sdk/export/metric"
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"go.opentelemetry.io/otel/sdk/export/metric/aggregation"
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"go.opentelemetry.io/otel/sdk/metric/aggregator"
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)
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// Note: This code uses a Mutex to govern access to the exclusive
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// aggregator state. This is in contrast to a lock-free approach
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// (as in the Go prometheus client) that was reverted here:
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// https://github.com/open-telemetry/opentelemetry-go/pull/669
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type (
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// Aggregator observe events and counts them in pre-determined buckets.
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// It also calculates the sum and count of all events.
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Aggregator struct {
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lock sync.Mutex
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boundaries []float64
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kind metric.NumberKind
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state state
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}
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// state represents the state of a histogram, consisting of
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// the sum and counts for all observed values and
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// the less than equal bucket count for the pre-determined boundaries.
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state struct {
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bucketCounts []float64
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count metric.Number
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sum metric.Number
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}
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)
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var _ export.Aggregator = &Aggregator{}
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var _ aggregation.Sum = &Aggregator{}
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var _ aggregation.Count = &Aggregator{}
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var _ aggregation.Histogram = &Aggregator{}
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// New returns a new aggregator for computing Histograms.
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//
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// A Histogram observe events and counts them in pre-defined buckets.
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// And also provides the total sum and count of all observations.
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//
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// Note that this aggregator maintains each value using independent
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// atomic operations, which introduces the possibility that
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// checkpoints are inconsistent.
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func New(cnt int, desc *metric.Descriptor, boundaries []float64) []Aggregator {
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aggs := make([]Aggregator, cnt)
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// Boundaries MUST be ordered otherwise the histogram could not
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// be properly computed.
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sortedBoundaries := make([]float64, len(boundaries))
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copy(sortedBoundaries, boundaries)
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sort.Float64s(sortedBoundaries)
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for i := range aggs {
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aggs[i] = Aggregator{
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kind: desc.NumberKind(),
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boundaries: sortedBoundaries,
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state: emptyState(sortedBoundaries),
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}
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}
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return aggs
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}
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// Aggregation returns an interface for reading the state of this aggregator.
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func (c *Aggregator) Aggregation() aggregation.Aggregation {
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return c
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}
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// Kind returns aggregation.HistogramKind.
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func (c *Aggregator) Kind() aggregation.Kind {
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return aggregation.HistogramKind
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}
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// Sum returns the sum of all values in the checkpoint.
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func (c *Aggregator) Sum() (metric.Number, error) {
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return c.state.sum, nil
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}
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// Count returns the number of values in the checkpoint.
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func (c *Aggregator) Count() (int64, error) {
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return int64(c.state.count), nil
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}
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// Histogram returns the count of events in pre-determined buckets.
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func (c *Aggregator) Histogram() (aggregation.Buckets, error) {
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return aggregation.Buckets{
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Boundaries: c.boundaries,
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Counts: c.state.bucketCounts,
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}, nil
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}
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// SynchronizedMove saves the current state into oa and resets the current state to
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// the empty set. Since no locks are taken, there is a chance that
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// the independent Sum, Count and Bucket Count are not consistent with each
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// other.
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func (c *Aggregator) SynchronizedMove(oa export.Aggregator, desc *metric.Descriptor) error {
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o, _ := oa.(*Aggregator)
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if o == nil {
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return aggregator.NewInconsistentAggregatorError(c, oa)
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}
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c.lock.Lock()
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o.state, c.state = c.state, emptyState(c.boundaries)
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c.lock.Unlock()
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return nil
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}
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func emptyState(boundaries []float64) state {
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return state{
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bucketCounts: make([]float64, len(boundaries)+1),
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}
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}
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// Update adds the recorded measurement to the current data set.
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func (c *Aggregator) Update(_ context.Context, number metric.Number, desc *metric.Descriptor) error {
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kind := desc.NumberKind()
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asFloat := number.CoerceToFloat64(kind)
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bucketID := len(c.boundaries)
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for i, boundary := range c.boundaries {
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if asFloat < boundary {
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bucketID = i
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break
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}
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}
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// Note: Binary-search was compared using the benchmarks. The following
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// code is equivalent to the linear search above:
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//
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// bucketID := sort.Search(len(c.boundaries), func(i int) bool {
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// return asFloat < c.boundaries[i]
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// })
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//
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// The binary search wins for very large boundary sets, but
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// the linear search performs better up through arrays between
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// 256 and 512 elements, which is a relatively large histogram, so we
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// continue to prefer linear search.
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c.lock.Lock()
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defer c.lock.Unlock()
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c.state.count.AddInt64(1)
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c.state.sum.AddNumber(kind, number)
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c.state.bucketCounts[bucketID]++
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return nil
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}
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// Merge combines two histograms that have the same buckets into a single one.
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func (c *Aggregator) Merge(oa export.Aggregator, desc *metric.Descriptor) error {
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o, _ := oa.(*Aggregator)
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if o == nil {
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return aggregator.NewInconsistentAggregatorError(c, oa)
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}
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c.state.sum.AddNumber(desc.NumberKind(), o.state.sum)
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c.state.count.AddNumber(metric.Uint64NumberKind, o.state.count)
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for i := 0; i < len(c.state.bucketCounts); i++ {
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c.state.bucketCounts[i] += o.state.bucketCounts[i]
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}
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return nil
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}
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