vendor: update buildkit to v0.18.0-rc1

Signed-off-by: Tonis Tiigi <tonistiigi@gmail.com>

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/doc.go

@@ -0,0 +1,6 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+// Package exemplar provides an implementation of the OpenTelemetry exemplar
+// reservoir to be used in metric collection pipelines.
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/drop.go

@@ -0,0 +1,23 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+// Drop returns a [FilteredReservoir] that drops all measurements it is offered.
+func Drop[N int64 | float64]() FilteredReservoir[N] { return &dropRes[N]{} }
+
+type dropRes[N int64 | float64] struct{}
+
+// Offer does nothing, all measurements offered will be dropped.
+func (r *dropRes[N]) Offer(context.Context, N, []attribute.KeyValue) {}
+
+// Collect resets dest. No exemplars will ever be returned.
+func (r *dropRes[N]) Collect(dest *[]Exemplar) {
+	*dest = (*dest)[:0]
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/exemplar.go

@@ -0,0 +1,29 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+// Exemplar is a measurement sampled from a timeseries providing a typical
+// example.
+type Exemplar struct {
+	// FilteredAttributes are the attributes recorded with the measurement but
+	// filtered out of the timeseries' aggregated data.
+	FilteredAttributes []attribute.KeyValue
+	// Time is the time when the measurement was recorded.
+	Time time.Time
+	// Value is the measured value.
+	Value Value
+	// SpanID is the ID of the span that was active during the measurement. If
+	// no span was active or the span was not sampled this will be empty.
+	SpanID []byte `json:",omitempty"`
+	// TraceID is the ID of the trace the active span belonged to during the
+	// measurement. If no span was active or the span was not sampled this will
+	// be empty.
+	TraceID []byte `json:",omitempty"`
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/filter.go

@@ -0,0 +1,29 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+
+	"go.opentelemetry.io/otel/trace"
+)
+
+// Filter determines if a measurement should be offered.
+//
+// The passed ctx needs to contain any baggage or span that were active
+// when the measurement was made. This information may be used by the
+// Reservoir in making a sampling decision.
+type Filter func(context.Context) bool
+
+// SampledFilter is a [Filter] that will only offer measurements
+// if the passed context associated with the measurement contains a sampled
+// [go.opentelemetry.io/otel/trace.SpanContext].
+func SampledFilter(ctx context.Context) bool {
+	return trace.SpanContextFromContext(ctx).IsSampled()
+}
+
+// AlwaysOnFilter is a [Filter] that always offers measurements.
+func AlwaysOnFilter(ctx context.Context) bool {
+	return true
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/filtered_reservoir.go

@@ -0,0 +1,49 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+// FilteredReservoir wraps a [Reservoir] with a filter.
+type FilteredReservoir[N int64 | float64] interface {
+	// Offer accepts the parameters associated with a measurement. The
+	// parameters will be stored as an exemplar if the filter decides to
+	// sample the measurement.
+	//
+	// The passed ctx needs to contain any baggage or span that were active
+	// when the measurement was made. This information may be used by the
+	// Reservoir in making a sampling decision.
+	Offer(ctx context.Context, val N, attr []attribute.KeyValue)
+	// Collect returns all the held exemplars in the reservoir.
+	Collect(dest *[]Exemplar)
+}
+
+// filteredReservoir handles the pre-sampled exemplar of measurements made.
+type filteredReservoir[N int64 | float64] struct {
+	filter    Filter
+	reservoir Reservoir
+}
+
+// NewFilteredReservoir creates a [FilteredReservoir] which only offers values
+// that are allowed by the filter.
+func NewFilteredReservoir[N int64 | float64](f Filter, r Reservoir) FilteredReservoir[N] {
+	return &filteredReservoir[N]{
+		filter:    f,
+		reservoir: r,
+	}
+}
+
+func (f *filteredReservoir[N]) Offer(ctx context.Context, val N, attr []attribute.KeyValue) {
+	if f.filter(ctx) {
+		// only record the current time if we are sampling this measurment.
+		f.reservoir.Offer(ctx, time.Now(), NewValue(val), attr)
+	}
+}
+
+func (f *filteredReservoir[N]) Collect(dest *[]Exemplar) { f.reservoir.Collect(dest) }

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/hist.go

@@ -0,0 +1,46 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+	"slices"
+	"sort"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+// Histogram returns a [Reservoir] that samples the last measurement that falls
+// within a histogram bucket. The histogram bucket upper-boundaries are define
+// by bounds.
+//
+// The passed bounds will be sorted by this function.
+func Histogram(bounds []float64) Reservoir {
+	slices.Sort(bounds)
+	return &histRes{
+		bounds:  bounds,
+		storage: newStorage(len(bounds) + 1),
+	}
+}
+
+type histRes struct {
+	*storage
+
+	// bounds are bucket bounds in ascending order.
+	bounds []float64
+}
+
+func (r *histRes) Offer(ctx context.Context, t time.Time, v Value, a []attribute.KeyValue) {
+	var x float64
+	switch v.Type() {
+	case Int64ValueType:
+		x = float64(v.Int64())
+	case Float64ValueType:
+		x = v.Float64()
+	default:
+		panic("unknown value type")
+	}
+	r.store[sort.SearchFloat64s(r.bounds, x)] = newMeasurement(ctx, t, v, a)
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/rand.go

@@ -0,0 +1,191 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+	"math"
+	"math/rand"
+	"sync"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+var (
+	// rng is used to make sampling decisions.
+	//
+	// Do not use crypto/rand. There is no reason for the decrease in performance
+	// given this is not a security sensitive decision.
+	rng = rand.New(rand.NewSource(time.Now().UnixNano()))
+	// Ensure concurrent safe accecess to rng and its underlying source.
+	rngMu sync.Mutex
+)
+
+// random returns, as a float64, a uniform pseudo-random number in the open
+// interval (0.0,1.0).
+func random() float64 {
+	// TODO: This does not return a uniform number. rng.Float64 returns a
+	// uniformly random int in [0,2^53) that is divided by 2^53. Meaning it
+	// returns multiples of 2^-53, and not all floating point numbers between 0
+	// and 1 (i.e. for values less than 2^-4 the 4 last bits of the significand
+	// are always going to be 0).
+	//
+	// An alternative algorithm should be considered that will actually return
+	// a uniform number in the interval (0,1). For example, since the default
+	// rand source provides a uniform distribution for Int63, this can be
+	// converted following the prototypical code of Mersenne Twister 64 (Takuji
+	// Nishimura and Makoto Matsumoto:
+	// http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/VERSIONS/C-LANG/mt19937-64.c)
+	//
+	//   (float64(rng.Int63()>>11) + 0.5) * (1.0 / 4503599627370496.0)
+	//
+	// There are likely many other methods to explore here as well.
+
+	rngMu.Lock()
+	defer rngMu.Unlock()
+
+	f := rng.Float64()
+	for f == 0 {
+		f = rng.Float64()
+	}
+	return f
+}
+
+// FixedSize returns a [Reservoir] that samples at most k exemplars. If there
+// are k or less measurements made, the Reservoir will sample each one. If
+// there are more than k, the Reservoir will then randomly sample all
+// additional measurement with a decreasing probability.
+func FixedSize(k int) Reservoir {
+	r := &randRes{storage: newStorage(k)}
+	r.reset()
+	return r
+}
+
+type randRes struct {
+	*storage
+
+	// count is the number of measurement seen.
+	count int64
+	// next is the next count that will store a measurement at a random index
+	// once the reservoir has been filled.
+	next int64
+	// w is the largest random number in a distribution that is used to compute
+	// the next next.
+	w float64
+}
+
+func (r *randRes) Offer(ctx context.Context, t time.Time, n Value, a []attribute.KeyValue) {
+	// The following algorithm is "Algorithm L" from Li, Kim-Hung (4 December
+	// 1994). "Reservoir-Sampling Algorithms of Time Complexity
+	// O(n(1+log(N/n)))". ACM Transactions on Mathematical Software. 20 (4):
+	// 481–493 (https://dl.acm.org/doi/10.1145/198429.198435).
+	//
+	// A high-level overview of "Algorithm L":
+	//   0) Pre-calculate the random count greater than the storage size when
+	//      an exemplar will be replaced.
+	//   1) Accept all measurements offered until the configured storage size is
+	//      reached.
+	//   2) Loop:
+	//      a) When the pre-calculate count is reached, replace a random
+	//         existing exemplar with the offered measurement.
+	//      b) Calculate the next random count greater than the existing one
+	//         which will replace another exemplars
+	//
+	// The way a "replacement" count is computed is by looking at `n` number of
+	// independent random numbers each corresponding to an offered measurement.
+	// Of these numbers the smallest `k` (the same size as the storage
+	// capacity) of them are kept as a subset. The maximum value in this
+	// subset, called `w` is used to weight another random number generation
+	// for the next count that will be considered.
+	//
+	// By weighting the next count computation like described, it is able to
+	// perform a uniformly-weighted sampling algorithm based on the number of
+	// samples the reservoir has seen so far. The sampling will "slow down" as
+	// more and more samples are offered so as to reduce a bias towards those
+	// offered just prior to the end of the collection.
+	//
+	// This algorithm is preferred because of its balance of simplicity and
+	// performance. It will compute three random numbers (the bulk of
+	// computation time) for each item that becomes part of the reservoir, but
+	// it does not spend any time on items that do not. In particular it has an
+	// asymptotic runtime of O(k(1 + log(n/k)) where n is the number of
+	// measurements offered and k is the reservoir size.
+	//
+	// See https://en.wikipedia.org/wiki/Reservoir_sampling for an overview of
+	// this and other reservoir sampling algorithms. See
+	// https://github.com/MrAlias/reservoir-sampling for a performance
+	// comparison of reservoir sampling algorithms.
+
+	if int(r.count) < cap(r.store) {
+		r.store[r.count] = newMeasurement(ctx, t, n, a)
+	} else {
+		if r.count == r.next {
+			// Overwrite a random existing measurement with the one offered.
+			idx := int(rng.Int63n(int64(cap(r.store))))
+			r.store[idx] = newMeasurement(ctx, t, n, a)
+			r.advance()
+		}
+	}
+	r.count++
+}
+
+// reset resets r to the initial state.
+func (r *randRes) reset() {
+	// This resets the number of exemplars known.
+	r.count = 0
+	// Random index inserts should only happen after the storage is full.
+	r.next = int64(cap(r.store))
+
+	// Initial random number in the series used to generate r.next.
+	//
+	// This is set before r.advance to reset or initialize the random number
+	// series. Without doing so it would always be 0 or never restart a new
+	// random number series.
+	//
+	// This maps the uniform random number in (0,1) to a geometric distribution
+	// over the same interval. The mean of the distribution is inversely
+	// proportional to the storage capacity.
+	r.w = math.Exp(math.Log(random()) / float64(cap(r.store)))
+
+	r.advance()
+}
+
+// advance updates the count at which the offered measurement will overwrite an
+// existing exemplar.
+func (r *randRes) advance() {
+	// Calculate the next value in the random number series.
+	//
+	// The current value of r.w is based on the max of a distribution of random
+	// numbers (i.e. `w = max(u_1,u_2,...,u_k)` for `k` equal to the capacity
+	// of the storage and each `u` in the interval (0,w)). To calculate the
+	// next r.w we use the fact that when the next exemplar is selected to be
+	// included in the storage an existing one will be dropped, and the
+	// corresponding random number in the set used to calculate r.w will also
+	// be replaced. The replacement random number will also be within (0,w),
+	// therefore the next r.w will be based on the same distribution (i.e.
+	// `max(u_1,u_2,...,u_k)`). Therefore, we can sample the next r.w by
+	// computing the next random number `u` and take r.w as `w * u^(1/k)`.
+	r.w *= math.Exp(math.Log(random()) / float64(cap(r.store)))
+	// Use the new random number in the series to calculate the count of the
+	// next measurement that will be stored.
+	//
+	// Given 0 < r.w < 1, each iteration will result in subsequent r.w being
+	// smaller. This translates here into the next next being selected against
+	// a distribution with a higher mean (i.e. the expected value will increase
+	// and replacements become less likely)
+	//
+	// Important to note, the new r.next will always be at least 1 more than
+	// the last r.next.
+	r.next += int64(math.Log(random())/math.Log(1-r.w)) + 1
+}
+
+func (r *randRes) Collect(dest *[]Exemplar) {
+	r.storage.Collect(dest)
+	// Call reset here even though it will reset r.count and restart the random
+	// number series. This will persist any old exemplars as long as no new
+	// measurements are offered, but it will also prioritize those new
+	// measurements that are made over the older collection cycle ones.
+	r.reset()
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/reservoir.go

@@ -0,0 +1,32 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+)
+
+// Reservoir holds the sampled exemplar of measurements made.
+type Reservoir interface {
+	// Offer accepts the parameters associated with a measurement. The
+	// parameters will be stored as an exemplar if the Reservoir decides to
+	// sample the measurement.
+	//
+	// The passed ctx needs to contain any baggage or span that were active
+	// when the measurement was made. This information may be used by the
+	// Reservoir in making a sampling decision.
+	//
+	// The time t is the time when the measurement was made. The val and attr
+	// parameters are the value and dropped (filtered) attributes of the
+	// measurement respectively.
+	Offer(ctx context.Context, t time.Time, val Value, attr []attribute.KeyValue)
+
+	// Collect returns all the held exemplars.
+	//
+	// The Reservoir state is preserved after this call.
+	Collect(dest *[]Exemplar)
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/storage.go

@@ -0,0 +1,95 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import (
+	"context"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/trace"
+)
+
+// storage is an exemplar storage for [Reservoir] implementations.
+type storage struct {
+	// store are the measurements sampled.
+	//
+	// This does not use []metricdata.Exemplar because it potentially would
+	// require an allocation for trace and span IDs in the hot path of Offer.
+	store []measurement
+}
+
+func newStorage(n int) *storage {
+	return &storage{store: make([]measurement, n)}
+}
+
+// Collect returns all the held exemplars.
+//
+// The Reservoir state is preserved after this call.
+func (r *storage) Collect(dest *[]Exemplar) {
+	*dest = reset(*dest, len(r.store), len(r.store))
+	var n int
+	for _, m := range r.store {
+		if !m.valid {
+			continue
+		}
+
+		m.Exemplar(&(*dest)[n])
+		n++
+	}
+	*dest = (*dest)[:n]
+}
+
+// measurement is a measurement made by a telemetry system.
+type measurement struct {
+	// FilteredAttributes are the attributes dropped during the measurement.
+	FilteredAttributes []attribute.KeyValue
+	// Time is the time when the measurement was made.
+	Time time.Time
+	// Value is the value of the measurement.
+	Value Value
+	// SpanContext is the SpanContext active when a measurement was made.
+	SpanContext trace.SpanContext
+
+	valid bool
+}
+
+// newMeasurement returns a new non-empty Measurement.
+func newMeasurement(ctx context.Context, ts time.Time, v Value, droppedAttr []attribute.KeyValue) measurement {
+	return measurement{
+		FilteredAttributes: droppedAttr,
+		Time:               ts,
+		Value:              v,
+		SpanContext:        trace.SpanContextFromContext(ctx),
+		valid:              true,
+	}
+}
+
+// Exemplar returns m as an [Exemplar].
+func (m measurement) Exemplar(dest *Exemplar) {
+	dest.FilteredAttributes = m.FilteredAttributes
+	dest.Time = m.Time
+	dest.Value = m.Value
+
+	if m.SpanContext.HasTraceID() {
+		traceID := m.SpanContext.TraceID()
+		dest.TraceID = traceID[:]
+	} else {
+		dest.TraceID = dest.TraceID[:0]
+	}
+
+	if m.SpanContext.HasSpanID() {
+		spanID := m.SpanContext.SpanID()
+		dest.SpanID = spanID[:]
+	} else {
+		dest.SpanID = dest.SpanID[:0]
+	}
+}
+
+func reset[T any](s []T, length, capacity int) []T {
+	if cap(s) < capacity {
+		return make([]T, length, capacity)
+	}
+	return s[:length]
+}

vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/value.go

@@ -0,0 +1,57 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+
+import "math"
+
+// ValueType identifies the type of value used in exemplar data.
+type ValueType uint8
+
+const (
+	// UnknownValueType should not be used. It represents a misconfigured
+	// Value.
+	UnknownValueType ValueType = 0
+	// Int64ValueType represents a Value with int64 data.
+	Int64ValueType ValueType = 1
+	// Float64ValueType represents a Value with float64 data.
+	Float64ValueType ValueType = 2
+)
+
+// Value is the value of data held by an exemplar.
+type Value struct {
+	t   ValueType
+	val uint64
+}
+
+// NewValue returns a new [Value] for the provided value.
+func NewValue[N int64 | float64](value N) Value {
+	switch v := any(value).(type) {
+	case int64:
+		return Value{t: Int64ValueType, val: uint64(v)}
+	case float64:
+		return Value{t: Float64ValueType, val: math.Float64bits(v)}
+	}
+	return Value{}
+}
+
+// Type returns the [ValueType] of data held by v.
+func (v Value) Type() ValueType { return v.t }
+
+// Int64 returns the value of v as an int64. If the ValueType of v is not an
+// Int64ValueType, 0 is returned.
+func (v Value) Int64() int64 {
+	if v.t == Int64ValueType {
+		return int64(v.val)
+	}
+	return 0
+}
+
+// Float64 returns the value of v as an float64. If the ValueType of v is not
+// an Float64ValueType, 0 is returned.
+func (v Value) Float64() float64 {
+	if v.t == Float64ValueType {
+		return math.Float64frombits(v.val)
+	}
+	return 0
+}