VOL-1558 Implementation of openolt adapter with dep for dependency management
Also updated the build system to take this into account.
Currently dep ensure fails due to missing libraries in voltha-go, but the vendor folder has been updated otherwise.
This can be worked around in development using the LOCAL_VOLTHAGO variable described in the readme
This does not build currrently, but that is due to missing code in voltha-go master.
This pattern is consistent with how voltha-go does things, but does not leave you dependent on it to build.
See the readme for how to use dep.
The resourcemanager file is no longer hidden.
Change-Id: I25b8472dbc517b193970597c9f43ddff18c2d89f
diff --git a/vendor/github.com/rcrowley/go-metrics/sample.go b/vendor/github.com/rcrowley/go-metrics/sample.go
new file mode 100644
index 0000000..fecee5e
--- /dev/null
+++ b/vendor/github.com/rcrowley/go-metrics/sample.go
@@ -0,0 +1,616 @@
+package metrics
+
+import (
+ "math"
+ "math/rand"
+ "sort"
+ "sync"
+ "time"
+)
+
+const rescaleThreshold = time.Hour
+
+// Samples maintain a statistically-significant selection of values from
+// a stream.
+type Sample interface {
+ Clear()
+ Count() int64
+ Max() int64
+ Mean() float64
+ Min() int64
+ Percentile(float64) float64
+ Percentiles([]float64) []float64
+ Size() int
+ Snapshot() Sample
+ StdDev() float64
+ Sum() int64
+ Update(int64)
+ Values() []int64
+ Variance() float64
+}
+
+// ExpDecaySample is an exponentially-decaying sample using a forward-decaying
+// priority reservoir. See Cormode et al's "Forward Decay: A Practical Time
+// Decay Model for Streaming Systems".
+//
+// <http://dimacs.rutgers.edu/~graham/pubs/papers/fwddecay.pdf>
+type ExpDecaySample struct {
+ alpha float64
+ count int64
+ mutex sync.Mutex
+ reservoirSize int
+ t0, t1 time.Time
+ values *expDecaySampleHeap
+}
+
+// NewExpDecaySample constructs a new exponentially-decaying sample with the
+// given reservoir size and alpha.
+func NewExpDecaySample(reservoirSize int, alpha float64) Sample {
+ if UseNilMetrics {
+ return NilSample{}
+ }
+ s := &ExpDecaySample{
+ alpha: alpha,
+ reservoirSize: reservoirSize,
+ t0: time.Now(),
+ values: newExpDecaySampleHeap(reservoirSize),
+ }
+ s.t1 = s.t0.Add(rescaleThreshold)
+ return s
+}
+
+// Clear clears all samples.
+func (s *ExpDecaySample) Clear() {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ s.count = 0
+ s.t0 = time.Now()
+ s.t1 = s.t0.Add(rescaleThreshold)
+ s.values.Clear()
+}
+
+// Count returns the number of samples recorded, which may exceed the
+// reservoir size.
+func (s *ExpDecaySample) Count() int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return s.count
+}
+
+// Max returns the maximum value in the sample, which may not be the maximum
+// value ever to be part of the sample.
+func (s *ExpDecaySample) Max() int64 {
+ return SampleMax(s.Values())
+}
+
+// Mean returns the mean of the values in the sample.
+func (s *ExpDecaySample) Mean() float64 {
+ return SampleMean(s.Values())
+}
+
+// Min returns the minimum value in the sample, which may not be the minimum
+// value ever to be part of the sample.
+func (s *ExpDecaySample) Min() int64 {
+ return SampleMin(s.Values())
+}
+
+// Percentile returns an arbitrary percentile of values in the sample.
+func (s *ExpDecaySample) Percentile(p float64) float64 {
+ return SamplePercentile(s.Values(), p)
+}
+
+// Percentiles returns a slice of arbitrary percentiles of values in the
+// sample.
+func (s *ExpDecaySample) Percentiles(ps []float64) []float64 {
+ return SamplePercentiles(s.Values(), ps)
+}
+
+// Size returns the size of the sample, which is at most the reservoir size.
+func (s *ExpDecaySample) Size() int {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return s.values.Size()
+}
+
+// Snapshot returns a read-only copy of the sample.
+func (s *ExpDecaySample) Snapshot() Sample {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ vals := s.values.Values()
+ values := make([]int64, len(vals))
+ for i, v := range vals {
+ values[i] = v.v
+ }
+ return &SampleSnapshot{
+ count: s.count,
+ values: values,
+ }
+}
+
+// StdDev returns the standard deviation of the values in the sample.
+func (s *ExpDecaySample) StdDev() float64 {
+ return SampleStdDev(s.Values())
+}
+
+// Sum returns the sum of the values in the sample.
+func (s *ExpDecaySample) Sum() int64 {
+ return SampleSum(s.Values())
+}
+
+// Update samples a new value.
+func (s *ExpDecaySample) Update(v int64) {
+ s.update(time.Now(), v)
+}
+
+// Values returns a copy of the values in the sample.
+func (s *ExpDecaySample) Values() []int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ vals := s.values.Values()
+ values := make([]int64, len(vals))
+ for i, v := range vals {
+ values[i] = v.v
+ }
+ return values
+}
+
+// Variance returns the variance of the values in the sample.
+func (s *ExpDecaySample) Variance() float64 {
+ return SampleVariance(s.Values())
+}
+
+// update samples a new value at a particular timestamp. This is a method all
+// its own to facilitate testing.
+func (s *ExpDecaySample) update(t time.Time, v int64) {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ s.count++
+ if s.values.Size() == s.reservoirSize {
+ s.values.Pop()
+ }
+ s.values.Push(expDecaySample{
+ k: math.Exp(t.Sub(s.t0).Seconds()*s.alpha) / rand.Float64(),
+ v: v,
+ })
+ if t.After(s.t1) {
+ values := s.values.Values()
+ t0 := s.t0
+ s.values.Clear()
+ s.t0 = t
+ s.t1 = s.t0.Add(rescaleThreshold)
+ for _, v := range values {
+ v.k = v.k * math.Exp(-s.alpha*s.t0.Sub(t0).Seconds())
+ s.values.Push(v)
+ }
+ }
+}
+
+// NilSample is a no-op Sample.
+type NilSample struct{}
+
+// Clear is a no-op.
+func (NilSample) Clear() {}
+
+// Count is a no-op.
+func (NilSample) Count() int64 { return 0 }
+
+// Max is a no-op.
+func (NilSample) Max() int64 { return 0 }
+
+// Mean is a no-op.
+func (NilSample) Mean() float64 { return 0.0 }
+
+// Min is a no-op.
+func (NilSample) Min() int64 { return 0 }
+
+// Percentile is a no-op.
+func (NilSample) Percentile(p float64) float64 { return 0.0 }
+
+// Percentiles is a no-op.
+func (NilSample) Percentiles(ps []float64) []float64 {
+ return make([]float64, len(ps))
+}
+
+// Size is a no-op.
+func (NilSample) Size() int { return 0 }
+
+// Sample is a no-op.
+func (NilSample) Snapshot() Sample { return NilSample{} }
+
+// StdDev is a no-op.
+func (NilSample) StdDev() float64 { return 0.0 }
+
+// Sum is a no-op.
+func (NilSample) Sum() int64 { return 0 }
+
+// Update is a no-op.
+func (NilSample) Update(v int64) {}
+
+// Values is a no-op.
+func (NilSample) Values() []int64 { return []int64{} }
+
+// Variance is a no-op.
+func (NilSample) Variance() float64 { return 0.0 }
+
+// SampleMax returns the maximum value of the slice of int64.
+func SampleMax(values []int64) int64 {
+ if 0 == len(values) {
+ return 0
+ }
+ var max int64 = math.MinInt64
+ for _, v := range values {
+ if max < v {
+ max = v
+ }
+ }
+ return max
+}
+
+// SampleMean returns the mean value of the slice of int64.
+func SampleMean(values []int64) float64 {
+ if 0 == len(values) {
+ return 0.0
+ }
+ return float64(SampleSum(values)) / float64(len(values))
+}
+
+// SampleMin returns the minimum value of the slice of int64.
+func SampleMin(values []int64) int64 {
+ if 0 == len(values) {
+ return 0
+ }
+ var min int64 = math.MaxInt64
+ for _, v := range values {
+ if min > v {
+ min = v
+ }
+ }
+ return min
+}
+
+// SamplePercentiles returns an arbitrary percentile of the slice of int64.
+func SamplePercentile(values int64Slice, p float64) float64 {
+ return SamplePercentiles(values, []float64{p})[0]
+}
+
+// SamplePercentiles returns a slice of arbitrary percentiles of the slice of
+// int64.
+func SamplePercentiles(values int64Slice, ps []float64) []float64 {
+ scores := make([]float64, len(ps))
+ size := len(values)
+ if size > 0 {
+ sort.Sort(values)
+ for i, p := range ps {
+ pos := p * float64(size+1)
+ if pos < 1.0 {
+ scores[i] = float64(values[0])
+ } else if pos >= float64(size) {
+ scores[i] = float64(values[size-1])
+ } else {
+ lower := float64(values[int(pos)-1])
+ upper := float64(values[int(pos)])
+ scores[i] = lower + (pos-math.Floor(pos))*(upper-lower)
+ }
+ }
+ }
+ return scores
+}
+
+// SampleSnapshot is a read-only copy of another Sample.
+type SampleSnapshot struct {
+ count int64
+ values []int64
+}
+
+func NewSampleSnapshot(count int64, values []int64) *SampleSnapshot {
+ return &SampleSnapshot{
+ count: count,
+ values: values,
+ }
+}
+
+// Clear panics.
+func (*SampleSnapshot) Clear() {
+ panic("Clear called on a SampleSnapshot")
+}
+
+// Count returns the count of inputs at the time the snapshot was taken.
+func (s *SampleSnapshot) Count() int64 { return s.count }
+
+// Max returns the maximal value at the time the snapshot was taken.
+func (s *SampleSnapshot) Max() int64 { return SampleMax(s.values) }
+
+// Mean returns the mean value at the time the snapshot was taken.
+func (s *SampleSnapshot) Mean() float64 { return SampleMean(s.values) }
+
+// Min returns the minimal value at the time the snapshot was taken.
+func (s *SampleSnapshot) Min() int64 { return SampleMin(s.values) }
+
+// Percentile returns an arbitrary percentile of values at the time the
+// snapshot was taken.
+func (s *SampleSnapshot) Percentile(p float64) float64 {
+ return SamplePercentile(s.values, p)
+}
+
+// Percentiles returns a slice of arbitrary percentiles of values at the time
+// the snapshot was taken.
+func (s *SampleSnapshot) Percentiles(ps []float64) []float64 {
+ return SamplePercentiles(s.values, ps)
+}
+
+// Size returns the size of the sample at the time the snapshot was taken.
+func (s *SampleSnapshot) Size() int { return len(s.values) }
+
+// Snapshot returns the snapshot.
+func (s *SampleSnapshot) Snapshot() Sample { return s }
+
+// StdDev returns the standard deviation of values at the time the snapshot was
+// taken.
+func (s *SampleSnapshot) StdDev() float64 { return SampleStdDev(s.values) }
+
+// Sum returns the sum of values at the time the snapshot was taken.
+func (s *SampleSnapshot) Sum() int64 { return SampleSum(s.values) }
+
+// Update panics.
+func (*SampleSnapshot) Update(int64) {
+ panic("Update called on a SampleSnapshot")
+}
+
+// Values returns a copy of the values in the sample.
+func (s *SampleSnapshot) Values() []int64 {
+ values := make([]int64, len(s.values))
+ copy(values, s.values)
+ return values
+}
+
+// Variance returns the variance of values at the time the snapshot was taken.
+func (s *SampleSnapshot) Variance() float64 { return SampleVariance(s.values) }
+
+// SampleStdDev returns the standard deviation of the slice of int64.
+func SampleStdDev(values []int64) float64 {
+ return math.Sqrt(SampleVariance(values))
+}
+
+// SampleSum returns the sum of the slice of int64.
+func SampleSum(values []int64) int64 {
+ var sum int64
+ for _, v := range values {
+ sum += v
+ }
+ return sum
+}
+
+// SampleVariance returns the variance of the slice of int64.
+func SampleVariance(values []int64) float64 {
+ if 0 == len(values) {
+ return 0.0
+ }
+ m := SampleMean(values)
+ var sum float64
+ for _, v := range values {
+ d := float64(v) - m
+ sum += d * d
+ }
+ return sum / float64(len(values))
+}
+
+// A uniform sample using Vitter's Algorithm R.
+//
+// <http://www.cs.umd.edu/~samir/498/vitter.pdf>
+type UniformSample struct {
+ count int64
+ mutex sync.Mutex
+ reservoirSize int
+ values []int64
+}
+
+// NewUniformSample constructs a new uniform sample with the given reservoir
+// size.
+func NewUniformSample(reservoirSize int) Sample {
+ if UseNilMetrics {
+ return NilSample{}
+ }
+ return &UniformSample{
+ reservoirSize: reservoirSize,
+ values: make([]int64, 0, reservoirSize),
+ }
+}
+
+// Clear clears all samples.
+func (s *UniformSample) Clear() {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ s.count = 0
+ s.values = make([]int64, 0, s.reservoirSize)
+}
+
+// Count returns the number of samples recorded, which may exceed the
+// reservoir size.
+func (s *UniformSample) Count() int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return s.count
+}
+
+// Max returns the maximum value in the sample, which may not be the maximum
+// value ever to be part of the sample.
+func (s *UniformSample) Max() int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleMax(s.values)
+}
+
+// Mean returns the mean of the values in the sample.
+func (s *UniformSample) Mean() float64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleMean(s.values)
+}
+
+// Min returns the minimum value in the sample, which may not be the minimum
+// value ever to be part of the sample.
+func (s *UniformSample) Min() int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleMin(s.values)
+}
+
+// Percentile returns an arbitrary percentile of values in the sample.
+func (s *UniformSample) Percentile(p float64) float64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SamplePercentile(s.values, p)
+}
+
+// Percentiles returns a slice of arbitrary percentiles of values in the
+// sample.
+func (s *UniformSample) Percentiles(ps []float64) []float64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SamplePercentiles(s.values, ps)
+}
+
+// Size returns the size of the sample, which is at most the reservoir size.
+func (s *UniformSample) Size() int {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return len(s.values)
+}
+
+// Snapshot returns a read-only copy of the sample.
+func (s *UniformSample) Snapshot() Sample {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ values := make([]int64, len(s.values))
+ copy(values, s.values)
+ return &SampleSnapshot{
+ count: s.count,
+ values: values,
+ }
+}
+
+// StdDev returns the standard deviation of the values in the sample.
+func (s *UniformSample) StdDev() float64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleStdDev(s.values)
+}
+
+// Sum returns the sum of the values in the sample.
+func (s *UniformSample) Sum() int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleSum(s.values)
+}
+
+// Update samples a new value.
+func (s *UniformSample) Update(v int64) {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ s.count++
+ if len(s.values) < s.reservoirSize {
+ s.values = append(s.values, v)
+ } else {
+ r := rand.Int63n(s.count)
+ if r < int64(len(s.values)) {
+ s.values[int(r)] = v
+ }
+ }
+}
+
+// Values returns a copy of the values in the sample.
+func (s *UniformSample) Values() []int64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ values := make([]int64, len(s.values))
+ copy(values, s.values)
+ return values
+}
+
+// Variance returns the variance of the values in the sample.
+func (s *UniformSample) Variance() float64 {
+ s.mutex.Lock()
+ defer s.mutex.Unlock()
+ return SampleVariance(s.values)
+}
+
+// expDecaySample represents an individual sample in a heap.
+type expDecaySample struct {
+ k float64
+ v int64
+}
+
+func newExpDecaySampleHeap(reservoirSize int) *expDecaySampleHeap {
+ return &expDecaySampleHeap{make([]expDecaySample, 0, reservoirSize)}
+}
+
+// expDecaySampleHeap is a min-heap of expDecaySamples.
+// The internal implementation is copied from the standard library's container/heap
+type expDecaySampleHeap struct {
+ s []expDecaySample
+}
+
+func (h *expDecaySampleHeap) Clear() {
+ h.s = h.s[:0]
+}
+
+func (h *expDecaySampleHeap) Push(s expDecaySample) {
+ n := len(h.s)
+ h.s = h.s[0 : n+1]
+ h.s[n] = s
+ h.up(n)
+}
+
+func (h *expDecaySampleHeap) Pop() expDecaySample {
+ n := len(h.s) - 1
+ h.s[0], h.s[n] = h.s[n], h.s[0]
+ h.down(0, n)
+
+ n = len(h.s)
+ s := h.s[n-1]
+ h.s = h.s[0 : n-1]
+ return s
+}
+
+func (h *expDecaySampleHeap) Size() int {
+ return len(h.s)
+}
+
+func (h *expDecaySampleHeap) Values() []expDecaySample {
+ return h.s
+}
+
+func (h *expDecaySampleHeap) up(j int) {
+ for {
+ i := (j - 1) / 2 // parent
+ if i == j || !(h.s[j].k < h.s[i].k) {
+ break
+ }
+ h.s[i], h.s[j] = h.s[j], h.s[i]
+ j = i
+ }
+}
+
+func (h *expDecaySampleHeap) down(i, n int) {
+ for {
+ j1 := 2*i + 1
+ if j1 >= n || j1 < 0 { // j1 < 0 after int overflow
+ break
+ }
+ j := j1 // left child
+ if j2 := j1 + 1; j2 < n && !(h.s[j1].k < h.s[j2].k) {
+ j = j2 // = 2*i + 2 // right child
+ }
+ if !(h.s[j].k < h.s[i].k) {
+ break
+ }
+ h.s[i], h.s[j] = h.s[j], h.s[i]
+ i = j
+ }
+}
+
+type int64Slice []int64
+
+func (p int64Slice) Len() int { return len(p) }
+func (p int64Slice) Less(i, j int) bool { return p[i] < p[j] }
+func (p int64Slice) Swap(i, j int) { p[i], p[j] = p[j], p[i] }