Update vendoring via vndr

gateway/vendor/github.com/beorn7/perks/.gitignore

@@ -1,2 +0,0 @@
-*.test
-*.prof

gateway/vendor/github.com/beorn7/perks/histogram/bench_test.go

@@ -1,26 +0,0 @@
-package histogram
-
-import (
-	"math/rand"
-	"testing"
-)
-
-func BenchmarkInsert10Bins(b *testing.B) {
-	b.StopTimer()
-	h := New(10)
-	b.StartTimer()
-	for i := 0; i < b.N; i++ {
-		f := rand.ExpFloat64()
-		h.Insert(f)
-	}
-}
-
-func BenchmarkInsert100Bins(b *testing.B) {
-	b.StopTimer()
-	h := New(100)
-	b.StartTimer()
-	for i := 0; i < b.N; i++ {
-		f := rand.ExpFloat64()
-		h.Insert(f)
-	}
-}

gateway/vendor/github.com/beorn7/perks/histogram/histogram.go

@@ -1,108 +0,0 @@
-// Package histogram provides a Go implementation of BigML's histogram package
-// for Clojure/Java. It is currently experimental.
-package histogram
-
-import (
-	"container/heap"
-	"math"
-	"sort"
-)
-
-type Bin struct {
-	Count int
-	Sum   float64
-}
-
-func (b *Bin) Update(x *Bin) {
-	b.Count += x.Count
-	b.Sum += x.Sum
-}
-
-func (b *Bin) Mean() float64 {
-	return b.Sum / float64(b.Count)
-}
-
-type Bins []*Bin
-
-func (bs Bins) Len() int           { return len(bs) }
-func (bs Bins) Less(i, j int) bool { return bs[i].Mean() < bs[j].Mean() }
-func (bs Bins) Swap(i, j int)      { bs[i], bs[j] = bs[j], bs[i] }
-
-func (bs *Bins) Push(x interface{}) {
-	*bs = append(*bs, x.(*Bin))
-}
-
-func (bs *Bins) Pop() interface{} {
-	return bs.remove(len(*bs) - 1)
-}
-
-func (bs *Bins) remove(n int) *Bin {
-	if n < 0 || len(*bs) < n {
-		return nil
-	}
-	x := (*bs)[n]
-	*bs = append((*bs)[:n], (*bs)[n+1:]...)
-	return x
-}
-
-type Histogram struct {
-	res *reservoir
-}
-
-func New(maxBins int) *Histogram {
-	return &Histogram{res: newReservoir(maxBins)}
-}
-
-func (h *Histogram) Insert(f float64) {
-	h.res.insert(&Bin{1, f})
-	h.res.compress()
-}
-
-func (h *Histogram) Bins() Bins {
-	return h.res.bins
-}
-
-type reservoir struct {
-	n       int
-	maxBins int
-	bins    Bins
-}
-
-func newReservoir(maxBins int) *reservoir {
-	return &reservoir{maxBins: maxBins}
-}
-
-func (r *reservoir) insert(bin *Bin) {
-	r.n += bin.Count
-	i := sort.Search(len(r.bins), func(i int) bool {
-		return r.bins[i].Mean() >= bin.Mean()
-	})
-	if i < 0 || i == r.bins.Len() {
-		// TODO(blake): Maybe use an .insert(i, bin) instead of
-		// performing the extra work of a heap.Push.
-		heap.Push(&r.bins, bin)
-		return
-	}
-	r.bins[i].Update(bin)
-}
-
-func (r *reservoir) compress() {
-	for r.bins.Len() > r.maxBins {
-		minGapIndex := -1
-		minGap := math.MaxFloat64
-		for i := 0; i < r.bins.Len()-1; i++ {
-			gap := gapWeight(r.bins[i], r.bins[i+1])
-			if minGap > gap {
-				minGap = gap
-				minGapIndex = i
-			}
-		}
-		prev := r.bins[minGapIndex]
-		next := r.bins.remove(minGapIndex + 1)
-		prev.Update(next)
-	}
-}
-
-func gapWeight(prev, next *Bin) float64 {
-	return next.Mean() - prev.Mean()
-}

gateway/vendor/github.com/beorn7/perks/histogram/histogram_test.go

@@ -1,38 +0,0 @@
-package histogram
-
-import (
-	"math/rand"
-	"testing"
-)
-
-func TestHistogram(t *testing.T) {
-	const numPoints = 1e6
-	const maxBins = 3
-
-	h := New(maxBins)
-	for i := 0; i < numPoints; i++ {
-		f := rand.ExpFloat64()
-		h.Insert(f)
-	}
-
-	bins := h.Bins()
-	if g := len(bins); g > maxBins {
-		t.Fatalf("got %d bins, wanted <= %d", g, maxBins)
-	}
-
-	for _, b := range bins {
-		t.Logf("%+v", b)
-	}
-
-	if g := count(h.Bins()); g != numPoints {
-		t.Fatalf("binned %d points, wanted %d", g, numPoints)
-	}
-}
-
-func count(bins Bins) int {
-	binCounts := 0
-	for _, b := range bins {
-		binCounts += b.Count
-	}
-	return binCounts
-}

gateway/vendor/github.com/beorn7/perks/quantile/bench_test.go

@@ -1,63 +0,0 @@
-package quantile
-
-import (
-	"testing"
-)
-
-func BenchmarkInsertTargeted(b *testing.B) {
-	b.ReportAllocs()
-
-	s := NewTargeted(Targets)
-	b.ResetTimer()
-	for i := float64(0); i < float64(b.N); i++ {
-		s.Insert(i)
-	}
-}
-
-func BenchmarkInsertTargetedSmallEpsilon(b *testing.B) {
-	s := NewTargeted(TargetsSmallEpsilon)
-	b.ResetTimer()
-	for i := float64(0); i < float64(b.N); i++ {
-		s.Insert(i)
-	}
-}
-
-func BenchmarkInsertBiased(b *testing.B) {
-	s := NewLowBiased(0.01)
-	b.ResetTimer()
-	for i := float64(0); i < float64(b.N); i++ {
-		s.Insert(i)
-	}
-}
-
-func BenchmarkInsertBiasedSmallEpsilon(b *testing.B) {
-	s := NewLowBiased(0.0001)
-	b.ResetTimer()
-	for i := float64(0); i < float64(b.N); i++ {
-		s.Insert(i)
-	}
-}
-
-func BenchmarkQuery(b *testing.B) {
-	s := NewTargeted(Targets)
-	for i := float64(0); i < 1e6; i++ {
-		s.Insert(i)
-	}
-	b.ResetTimer()
-	n := float64(b.N)
-	for i := float64(0); i < n; i++ {
-		s.Query(i / n)
-	}
-}
-
-func BenchmarkQuerySmallEpsilon(b *testing.B) {
-	s := NewTargeted(TargetsSmallEpsilon)
-	for i := float64(0); i < 1e6; i++ {
-		s.Insert(i)
-	}
-	b.ResetTimer()
-	n := float64(b.N)
-	for i := float64(0); i < n; i++ {
-		s.Query(i / n)
-	}
-}

gateway/vendor/github.com/beorn7/perks/quantile/example_test.go

@@ -1,121 +0,0 @@
-// +build go1.1
-
-package quantile_test
-
-import (
-	"bufio"
-	"fmt"
-	"log"
-	"os"
-	"strconv"
-	"time"
-
-	"github.com/beorn7/perks/quantile"
-)
-
-func Example_simple() {
-	ch := make(chan float64)
-	go sendFloats(ch)
-
-	// Compute the 50th, 90th, and 99th percentile.
-	q := quantile.NewTargeted(map[float64]float64{
-		0.50: 0.005,
-		0.90: 0.001,
-		0.99: 0.0001,
-	})
-	for v := range ch {
-		q.Insert(v)
-	}
-
-	fmt.Println("perc50:", q.Query(0.50))
-	fmt.Println("perc90:", q.Query(0.90))
-	fmt.Println("perc99:", q.Query(0.99))
-	fmt.Println("count:", q.Count())
-	// Output:
-	// perc50: 5
-	// perc90: 16
-	// perc99: 223
-	// count: 2388
-}
-
-func Example_mergeMultipleStreams() {
-	// Scenario:
-	// We have multiple database shards. On each shard, there is a process
-	// collecting query response times from the database logs and inserting
-	// them into a Stream (created via NewTargeted(0.90)), much like the
-	// Simple example. These processes expose a network interface for us to
-	// ask them to serialize and send us the results of their
-	// Stream.Samples so we may Merge and Query them.
-	//
-	// NOTES:
-	// * These sample sets are small, allowing us to get them
-	// across the network much faster than sending the entire list of data
-	// points.
-	//
-	// * For this to work correctly, we must supply the same quantiles
-	// a priori the process collecting the samples supplied to NewTargeted,
-	// even if we do not plan to query them all here.
-	ch := make(chan quantile.Samples)
-	getDBQuerySamples(ch)
-	q := quantile.NewTargeted(map[float64]float64{0.90: 0.001})
-	for samples := range ch {
-		q.Merge(samples)
-	}
-	fmt.Println("perc90:", q.Query(0.90))
-}
-
-func Example_window() {
-	// Scenario: We want the 90th, 95th, and 99th percentiles for each
-	// minute.
-
-	ch := make(chan float64)
-	go sendStreamValues(ch)
-
-	tick := time.NewTicker(1 * time.Minute)
-	q := quantile.NewTargeted(map[float64]float64{
-		0.90: 0.001,
-		0.95: 0.0005,
-		0.99: 0.0001,
-	})
-	for {
-		select {
-		case t := <-tick.C:
-			flushToDB(t, q.Samples())
-			q.Reset()
-		case v := <-ch:
-			q.Insert(v)
-		}
-	}
-}
-
-func sendStreamValues(ch chan float64) {
-	// Use your imagination
-}
-
-func flushToDB(t time.Time, samples quantile.Samples) {
-	// Use your imagination
-}
-
-// This is a stub for the above example. In reality this would hit the remote
-// servers via http or something like it.
-func getDBQuerySamples(ch chan quantile.Samples) {}
-
-func sendFloats(ch chan<- float64) {
-	f, err := os.Open("exampledata.txt")
-	if err != nil {
-		log.Fatal(err)
-	}
-	sc := bufio.NewScanner(f)
-	for sc.Scan() {
-		b := sc.Bytes()
-		v, err := strconv.ParseFloat(string(b), 64)
-		if err != nil {
-			log.Fatal(err)
-		}
-		ch <- v
-	}
-	if sc.Err() != nil {
-		log.Fatal(sc.Err())
-	}
-	close(ch)
-}

gateway/vendor/github.com/beorn7/perks/quantile/stream_test.go

@@ -1,215 +0,0 @@
-package quantile
-
-import (
-	"math"
-	"math/rand"
-	"sort"
-	"testing"
-)
-
-var (
-	Targets = map[float64]float64{
-		0.01: 0.001,
-		0.10: 0.01,
-		0.50: 0.05,
-		0.90: 0.01,
-		0.99: 0.001,
-	}
-	TargetsSmallEpsilon = map[float64]float64{
-		0.01: 0.0001,
-		0.10: 0.001,
-		0.50: 0.005,
-		0.90: 0.001,
-		0.99: 0.0001,
-	}
-	LowQuantiles  = []float64{0.01, 0.1, 0.5}
-	HighQuantiles = []float64{0.99, 0.9, 0.5}
-)
-
-const RelativeEpsilon = 0.01
-
-func verifyPercsWithAbsoluteEpsilon(t *testing.T, a []float64, s *Stream) {
-	sort.Float64s(a)
-	for quantile, epsilon := range Targets {
-		n := float64(len(a))
-		k := int(quantile * n)
-		if k < 1 {
-			k = 1
-		}
-		lower := int((quantile - epsilon) * n)
-		if lower < 1 {
-			lower = 1
-		}
-		upper := int(math.Ceil((quantile + epsilon) * n))
-		if upper > len(a) {
-			upper = len(a)
-		}
-		w, min, max := a[k-1], a[lower-1], a[upper-1]
-		if g := s.Query(quantile); g < min || g > max {
-			t.Errorf("q=%f: want %v [%f,%f], got %v", quantile, w, min, max, g)
-		}
-	}
-}
-
-func verifyLowPercsWithRelativeEpsilon(t *testing.T, a []float64, s *Stream) {
-	sort.Float64s(a)
-	for _, qu := range LowQuantiles {
-		n := float64(len(a))
-		k := int(qu * n)
-
-		lowerRank := int((1 - RelativeEpsilon) * qu * n)
-		upperRank := int(math.Ceil((1 + RelativeEpsilon) * qu * n))
-		w, min, max := a[k-1], a[lowerRank-1], a[upperRank-1]
-		if g := s.Query(qu); g < min || g > max {
-			t.Errorf("q=%f: want %v [%f,%f], got %v", qu, w, min, max, g)
-		}
-	}
-}
-
-func verifyHighPercsWithRelativeEpsilon(t *testing.T, a []float64, s *Stream) {
-	sort.Float64s(a)
-	for _, qu := range HighQuantiles {
-		n := float64(len(a))
-		k := int(qu * n)
-
-		lowerRank := int((1 - (1+RelativeEpsilon)*(1-qu)) * n)
-		upperRank := int(math.Ceil((1 - (1-RelativeEpsilon)*(1-qu)) * n))
-		w, min, max := a[k-1], a[lowerRank-1], a[upperRank-1]
-		if g := s.Query(qu); g < min || g > max {
-			t.Errorf("q=%f: want %v [%f,%f], got %v", qu, w, min, max, g)
-		}
-	}
-}
-
-func populateStream(s *Stream) []float64 {
-	a := make([]float64, 0, 1e5+100)
-	for i := 0; i < cap(a); i++ {
-		v := rand.NormFloat64()
-		// Add 5% asymmetric outliers.
-		if i%20 == 0 {
-			v = v*v + 1
-		}
-		s.Insert(v)
-		a = append(a, v)
-	}
-	return a
-}
-
-func TestTargetedQuery(t *testing.T) {
-	rand.Seed(42)
-	s := NewTargeted(Targets)
-	a := populateStream(s)
-	verifyPercsWithAbsoluteEpsilon(t, a, s)
-}
-
-func TestTargetedQuerySmallSampleSize(t *testing.T) {
-	rand.Seed(42)
-	s := NewTargeted(TargetsSmallEpsilon)
-	a := []float64{1, 2, 3, 4, 5}
-	for _, v := range a {
-		s.Insert(v)
-	}
-	verifyPercsWithAbsoluteEpsilon(t, a, s)
-	// If not yet flushed, results should be precise:
-	if !s.flushed() {
-		for φ, want := range map[float64]float64{
-			0.01: 1,
-			0.10: 1,
-			0.50: 3,
-			0.90: 5,
-			0.99: 5,
-		} {
-			if got := s.Query(φ); got != want {
-				t.Errorf("want %f for φ=%f, got %f", want, φ, got)
-			}
-		}
-	}
-}
-
-func TestLowBiasedQuery(t *testing.T) {
-	rand.Seed(42)
-	s := NewLowBiased(RelativeEpsilon)
-	a := populateStream(s)
-	verifyLowPercsWithRelativeEpsilon(t, a, s)
-}
-
-func TestHighBiasedQuery(t *testing.T) {
-	rand.Seed(42)
-	s := NewHighBiased(RelativeEpsilon)
-	a := populateStream(s)
-	verifyHighPercsWithRelativeEpsilon(t, a, s)
-}
-
-// BrokenTestTargetedMerge is broken, see Merge doc comment.
-func BrokenTestTargetedMerge(t *testing.T) {
-	rand.Seed(42)
-	s1 := NewTargeted(Targets)
-	s2 := NewTargeted(Targets)
-	a := populateStream(s1)
-	a = append(a, populateStream(s2)...)
-	s1.Merge(s2.Samples())
-	verifyPercsWithAbsoluteEpsilon(t, a, s1)
-}
-
-// BrokenTestLowBiasedMerge is broken, see Merge doc comment.
-func BrokenTestLowBiasedMerge(t *testing.T) {
-	rand.Seed(42)
-	s1 := NewLowBiased(RelativeEpsilon)
-	s2 := NewLowBiased(RelativeEpsilon)
-	a := populateStream(s1)
-	a = append(a, populateStream(s2)...)
-	s1.Merge(s2.Samples())
-	verifyLowPercsWithRelativeEpsilon(t, a, s2)
-}
-
-// BrokenTestHighBiasedMerge is broken, see Merge doc comment.
-func BrokenTestHighBiasedMerge(t *testing.T) {
-	rand.Seed(42)
-	s1 := NewHighBiased(RelativeEpsilon)
-	s2 := NewHighBiased(RelativeEpsilon)
-	a := populateStream(s1)
-	a = append(a, populateStream(s2)...)
-	s1.Merge(s2.Samples())
-	verifyHighPercsWithRelativeEpsilon(t, a, s2)
-}
-
-func TestUncompressed(t *testing.T) {
-	q := NewTargeted(Targets)
-	for i := 100; i > 0; i-- {
-		q.Insert(float64(i))
-	}
-	if g := q.Count(); g != 100 {
-		t.Errorf("want count 100, got %d", g)
-	}
-	// Before compression, Query should have 100% accuracy.
-	for quantile := range Targets {
-		w := quantile * 100
-		if g := q.Query(quantile); g != w {
-			t.Errorf("want %f, got %f", w, g)
-		}
-	}
-}
-
-func TestUncompressedSamples(t *testing.T) {
-	q := NewTargeted(map[float64]float64{0.99: 0.001})
-	for i := 1; i <= 100; i++ {
-		q.Insert(float64(i))
-	}
-	if g := q.Samples().Len(); g != 100 {
-		t.Errorf("want count 100, got %d", g)
-	}
-}
-
-func TestUncompressedOne(t *testing.T) {
-	q := NewTargeted(map[float64]float64{0.99: 0.01})
-	q.Insert(3.14)
-	if g := q.Query(0.90); g != 3.14 {
-		t.Error("want PI, got", g)
-	}
-}
-
-func TestDefaults(t *testing.T) {
-	if g := NewTargeted(map[float64]float64{0.99: 0.001}).Query(0.99); g != 0 {
-		t.Errorf("want 0, got %f", g)
-	}
-}

gateway/vendor/github.com/beorn7/perks/topk/topk.go

@@ -1,90 +0,0 @@
-package topk
-
-import (
-	"sort"
-)
-
-// http://www.cs.ucsb.edu/research/tech_reports/reports/2005-23.pdf
-
-type Element struct {
-	Value string
-	Count int
-}
-
-type Samples []*Element
-
-func (sm Samples) Len() int {
-	return len(sm)
-}
-
-func (sm Samples) Less(i, j int) bool {
-	return sm[i].Count < sm[j].Count
-}
-
-func (sm Samples) Swap(i, j int) {
-	sm[i], sm[j] = sm[j], sm[i]
-}
-
-type Stream struct {
-	k   int
-	mon map[string]*Element
-
-	// the minimum Element
-	min *Element
-}
-
-func New(k int) *Stream {
-	s := new(Stream)
-	s.k = k
-	s.mon = make(map[string]*Element)
-	s.min = &Element{}
-
-	// Track k+1 so that less frequenet items contended for that spot,
-	// resulting in k being more accurate.
-	return s
-}
-
-func (s *Stream) Insert(x string) {
-	s.insert(&Element{x, 1})
-}
-
-func (s *Stream) Merge(sm Samples) {
-	for _, e := range sm {
-		s.insert(e)
-	}
-}
-
-func (s *Stream) insert(in *Element) {
-	e := s.mon[in.Value]
-	if e != nil {
-		e.Count++
-	} else {
-		if len(s.mon) < s.k+1 {
-			e = &Element{in.Value, in.Count}
-			s.mon[in.Value] = e
-		} else {
-			e = s.min
-			delete(s.mon, e.Value)
-			e.Value = in.Value
-			e.Count += in.Count
-			s.min = e
-		}
-	}
-	if e.Count < s.min.Count {
-		s.min = e
-	}
-}
-
-func (s *Stream) Query() Samples {
-	var sm Samples
-	for _, e := range s.mon {
-		sm = append(sm, e)
-	}
-	sort.Sort(sort.Reverse(sm))
-
-	if len(sm) < s.k {
-		return sm
-	}
-
-	return sm[:s.k]
-}

gateway/vendor/github.com/beorn7/perks/topk/topk_test.go

@@ -1,57 +0,0 @@
-package topk
-
-import (
-	"fmt"
-	"math/rand"
-	"sort"
-	"testing"
-)
-
-func TestTopK(t *testing.T) {
-	stream := New(10)
-	ss := []*Stream{New(10), New(10), New(10)}
-	m := make(map[string]int)
-	for _, s := range ss {
-		for i := 0; i < 1e6; i++ {
-			v := fmt.Sprintf("%x", int8(rand.ExpFloat64()))
-			s.Insert(v)
-			m[v]++
-		}
-		stream.Merge(s.Query())
-	}
-
-	var sm Samples
-	for x, s := range m {
-		sm = append(sm, &Element{x, s})
-	}
-	sort.Sort(sort.Reverse(sm))
-
-	g := stream.Query()
-	if len(g) != 10 {
-		t.Fatalf("got %d, want 10", len(g))
-	}
-	for i, e := range g {
-		if sm[i].Value != e.Value {
-			t.Errorf("at %d: want %q, got %q", i, sm[i].Value, e.Value)
-		}
-	}
-}
-
-func TestQuery(t *testing.T) {
-	queryTests := []struct {
-		value string
-		expected int
-	}{
-		{"a", 1},
-		{"b", 2},
-		{"c", 2},
-	}
-
-	stream := New(2)
-	for _, tt := range queryTests {
-		stream.Insert(tt.value)
-		if n := len(stream.Query()); n != tt.expected {
-			t.Errorf("want %d, got %d", tt.expected, n)
-		}
-	}
-}