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vendor: update buildkit to v0.18.0-rc1

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Signed-off-by: Tonis Tiigi <tonistiigi@gmail.com>
This commit is contained in:
Tonis Tiigi
2024-11-21 12:57:27 -08:00
parent 1a039115bc
commit 13a426fca6
448 changed files with 35377 additions and 5707 deletions
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106
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go generated vendored
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@ -1,30 +1,24 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import (
"context"
"slices"
"sort"
"sync"
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
type buckets[N int64 | float64] struct {
attrs attribute.Set
res exemplar.FilteredReservoir[N]
counts []uint64
count uint64
total N
@ -32,8 +26,8 @@ type buckets[N int64 | float64] struct {
}
// newBuckets returns buckets with n bins.
func newBuckets[N int64 | float64](n int) *buckets[N] {
return &buckets[N]{counts: make([]uint64, n)}
func newBuckets[N int64 | float64](attrs attribute.Set, n int) *buckets[N] {
return &buckets[N]{attrs: attrs, counts: make([]uint64, n)}
}
func (b *buckets[N]) sum(value N) { b.total += value }
@ -54,28 +48,31 @@ type histValues[N int64 | float64] struct {
noSum bool
bounds []float64
values map[attribute.Set]*buckets[N]
newRes func() exemplar.FilteredReservoir[N]
limit limiter[*buckets[N]]
values map[attribute.Distinct]*buckets[N]
valuesMu sync.Mutex
}
func newHistValues[N int64 | float64](bounds []float64, noSum bool) *histValues[N] {
func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *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)
b := slices.Clone(bounds)
slices.Sort(b)
return &histValues[N]{
noSum: noSum,
bounds: b,
values: make(map[attribute.Set]*buckets[N]),
newRes: r,
limit: newLimiter[*buckets[N]](limit),
values: make(map[attribute.Distinct]*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) {
func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
// 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
@ -86,7 +83,8 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
s.valuesMu.Lock()
defer s.valuesMu.Unlock()
b, ok := s.values[attr]
attr := s.limit.Attributes(fltrAttr, s.values)
b, ok := s.values[attr.Equivalent()]
if !ok {
// N+1 buckets. For example:
//
@ -95,22 +93,25 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
// Then,
//
// buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
b = newBuckets[N](len(s.bounds) + 1)
b = newBuckets[N](attr, len(s.bounds)+1)
b.res = s.newRes()
// Ensure min and max are recorded values (not zero), for new buckets.
b.min, b.max = value, value
s.values[attr] = b
s.values[attr.Equivalent()] = b
}
b.bin(idx, value)
if !s.noSum {
b.sum(value)
}
b.res.Offer(ctx, value, droppedAttr)
}
// 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] {
func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *histogram[N] {
return &histogram[N]{
histValues: newHistValues[N](boundaries, noSum),
histValues: newHistValues[N](boundaries, noSum, limit, r),
noMinMax: noMinMax,
start: now(),
}
@ -137,34 +138,35 @@ func (s *histogram[N]) delta(dest *metricdata.Aggregation) int {
defer s.valuesMu.Unlock()
// Do not allow modification of our copy of bounds.
bounds := make([]float64, len(s.bounds))
copy(bounds, s.bounds)
bounds := slices.Clone(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
for _, val := range s.values {
hDPts[i].Attributes = val.attrs
hDPts[i].StartTime = s.start
hDPts[i].Time = t
hDPts[i].Count = b.count
hDPts[i].Count = val.count
hDPts[i].Bounds = bounds
hDPts[i].BucketCounts = b.counts
hDPts[i].BucketCounts = val.counts
if !s.noSum {
hDPts[i].Sum = b.total
hDPts[i].Sum = val.total
}
if !s.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
hDPts[i].Min = metricdata.NewExtrema(val.min)
hDPts[i].Max = metricdata.NewExtrema(val.max)
}
// Unused attribute sets do not report.
delete(s.values, a)
collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
i++
}
// Unused attribute sets do not report.
clear(s.values)
// The delta collection cycle resets.
s.start = t
@ -186,37 +188,37 @@ func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int {
defer s.valuesMu.Unlock()
// Do not allow modification of our copy of bounds.
bounds := make([]float64, len(s.bounds))
copy(bounds, s.bounds)
bounds := slices.Clone(s.bounds)
n := len(s.values)
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range s.values {
for _, val := range s.values {
hDPts[i].Attributes = val.attrs
hDPts[i].StartTime = s.start
hDPts[i].Time = t
hDPts[i].Count = val.count
hDPts[i].Bounds = bounds
// 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
hDPts[i].BucketCounts = slices.Clone(val.counts)
if !s.noSum {
hDPts[i].Sum = b.total
hDPts[i].Sum = val.total
}
if !s.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
hDPts[i].Min = metricdata.NewExtrema(val.min)
hDPts[i].Max = metricdata.NewExtrema(val.max)
}
collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
i++
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute