vendor: regenerate vendor directory with glide

This commit is contained in:
Jimmy Zelinskie 2017-05-05 11:42:38 -04:00
parent d846c508c3
commit 35df9d5846
1019 changed files with 288420 additions and 0 deletions

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vendor/github.com/beorn7/perks/.gitignore generated vendored Normal file
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*.test
*.prof

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vendor/github.com/beorn7/perks/LICENSE generated vendored Normal file
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Copyright (C) 2013 Blake Mizerany
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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vendor/github.com/beorn7/perks/README.md generated vendored Normal file
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# Perks for Go (golang.org)
Perks contains the Go package quantile that computes approximate quantiles over
an unbounded data stream within low memory and CPU bounds.
For more information and examples, see:
http://godoc.org/github.com/bmizerany/perks
A very special thank you and shout out to Graham Cormode (Rutgers University),
Flip Korn (AT&T LabsResearch), S. Muthukrishnan (Rutgers University), and
Divesh Srivastava (AT&T LabsResearch) for their research and publication of
[Effective Computation of Biased Quantiles over Data Streams](http://www.cs.rutgers.edu/~muthu/bquant.pdf)
Thank you, also:
* Armon Dadgar (@armon)
* Andrew Gerrand (@nf)
* Brad Fitzpatrick (@bradfitz)
* Keith Rarick (@kr)
FAQ:
Q: Why not move the quantile package into the project root?
A: I want to add more packages to perks later.
Copyright (C) 2013 Blake Mizerany
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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vendor/github.com/beorn7/perks/histogram/bench_test.go generated vendored Normal file
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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)
}
}

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vendor/github.com/beorn7/perks/histogram/histogram.go generated vendored Normal file
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// 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()
}

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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
}

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vendor/github.com/beorn7/perks/quantile/bench_test.go generated vendored Normal file
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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)
}
}

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// +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)
}

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vendor/github.com/beorn7/perks/quantile/stream.go generated vendored Normal file
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// Package quantile computes approximate quantiles over an unbounded data
// stream within low memory and CPU bounds.
//
// A small amount of accuracy is traded to achieve the above properties.
//
// Multiple streams can be merged before calling Query to generate a single set
// of results. This is meaningful when the streams represent the same type of
// data. See Merge and Samples.
//
// For more detailed information about the algorithm used, see:
//
// Effective Computation of Biased Quantiles over Data Streams
//
// http://www.cs.rutgers.edu/~muthu/bquant.pdf
package quantile
import (
"math"
"sort"
)
// Sample holds an observed value and meta information for compression. JSON
// tags have been added for convenience.
type Sample struct {
Value float64 `json:",string"`
Width float64 `json:",string"`
Delta float64 `json:",string"`
}
// Samples represents a slice of samples. It implements sort.Interface.
type Samples []Sample
func (a Samples) Len() int { return len(a) }
func (a Samples) Less(i, j int) bool { return a[i].Value < a[j].Value }
func (a Samples) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
type invariant func(s *stream, r float64) float64
// NewLowBiased returns an initialized Stream for low-biased quantiles
// (e.g. 0.01, 0.1, 0.5) where the needed quantiles are not known a priori, but
// error guarantees can still be given even for the lower ranks of the data
// distribution.
//
// The provided epsilon is a relative error, i.e. the true quantile of a value
// returned by a query is guaranteed to be within (1±Epsilon)*Quantile.
//
// See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error
// properties.
func NewLowBiased(epsilon float64) *Stream {
ƒ := func(s *stream, r float64) float64 {
return 2 * epsilon * r
}
return newStream(ƒ)
}
// NewHighBiased returns an initialized Stream for high-biased quantiles
// (e.g. 0.01, 0.1, 0.5) where the needed quantiles are not known a priori, but
// error guarantees can still be given even for the higher ranks of the data
// distribution.
//
// The provided epsilon is a relative error, i.e. the true quantile of a value
// returned by a query is guaranteed to be within 1-(1±Epsilon)*(1-Quantile).
//
// See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error
// properties.
func NewHighBiased(epsilon float64) *Stream {
ƒ := func(s *stream, r float64) float64 {
return 2 * epsilon * (s.n - r)
}
return newStream(ƒ)
}
// NewTargeted returns an initialized Stream concerned with a particular set of
// quantile values that are supplied a priori. Knowing these a priori reduces
// space and computation time. The targets map maps the desired quantiles to
// their absolute errors, i.e. the true quantile of a value returned by a query
// is guaranteed to be within (Quantile±Epsilon).
//
// See http://www.cs.rutgers.edu/~muthu/bquant.pdf for time, space, and error properties.
func NewTargeted(targets map[float64]float64) *Stream {
ƒ := func(s *stream, r float64) float64 {
var m = math.MaxFloat64
var f float64
for quantile, epsilon := range targets {
if quantile*s.n <= r {
f = (2 * epsilon * r) / quantile
} else {
f = (2 * epsilon * (s.n - r)) / (1 - quantile)
}
if f < m {
m = f
}
}
return m
}
return newStream(ƒ)
}
// Stream computes quantiles for a stream of float64s. It is not thread-safe by
// design. Take care when using across multiple goroutines.
type Stream struct {
*stream
b Samples
sorted bool
}
func newStream(ƒ invariant) *Stream {
x := &stream{ƒ: ƒ}
return &Stream{x, make(Samples, 0, 500), true}
}
// Insert inserts v into the stream.
func (s *Stream) Insert(v float64) {
s.insert(Sample{Value: v, Width: 1})
}
func (s *Stream) insert(sample Sample) {
s.b = append(s.b, sample)
s.sorted = false
if len(s.b) == cap(s.b) {
s.flush()
}
}
// Query returns the computed qth percentiles value. If s was created with
// NewTargeted, and q is not in the set of quantiles provided a priori, Query
// will return an unspecified result.
func (s *Stream) Query(q float64) float64 {
if !s.flushed() {
// Fast path when there hasn't been enough data for a flush;
// this also yields better accuracy for small sets of data.
l := len(s.b)
if l == 0 {
return 0
}
i := int(math.Ceil(float64(l) * q))
if i > 0 {
i -= 1
}
s.maybeSort()
return s.b[i].Value
}
s.flush()
return s.stream.query(q)
}
// Merge merges samples into the underlying streams samples. This is handy when
// merging multiple streams from separate threads, database shards, etc.
//
// ATTENTION: This method is broken and does not yield correct results. The
// underlying algorithm is not capable of merging streams correctly.
func (s *Stream) Merge(samples Samples) {
sort.Sort(samples)
s.stream.merge(samples)
}
// Reset reinitializes and clears the list reusing the samples buffer memory.
func (s *Stream) Reset() {
s.stream.reset()
s.b = s.b[:0]
}
// Samples returns stream samples held by s.
func (s *Stream) Samples() Samples {
if !s.flushed() {
return s.b
}
s.flush()
return s.stream.samples()
}
// Count returns the total number of samples observed in the stream
// since initialization.
func (s *Stream) Count() int {
return len(s.b) + s.stream.count()
}
func (s *Stream) flush() {
s.maybeSort()
s.stream.merge(s.b)
s.b = s.b[:0]
}
func (s *Stream) maybeSort() {
if !s.sorted {
s.sorted = true
sort.Sort(s.b)
}
}
func (s *Stream) flushed() bool {
return len(s.stream.l) > 0
}
type stream struct {
n float64
l []Sample
ƒ invariant
}
func (s *stream) reset() {
s.l = s.l[:0]
s.n = 0
}
func (s *stream) insert(v float64) {
s.merge(Samples{{v, 1, 0}})
}
func (s *stream) merge(samples Samples) {
// TODO(beorn7): This tries to merge not only individual samples, but
// whole summaries. The paper doesn't mention merging summaries at
// all. Unittests show that the merging is inaccurate. Find out how to
// do merges properly.
var r float64
i := 0
for _, sample := range samples {
for ; i < len(s.l); i++ {
c := s.l[i]
if c.Value > sample.Value {
// Insert at position i.
s.l = append(s.l, Sample{})
copy(s.l[i+1:], s.l[i:])
s.l[i] = Sample{
sample.Value,
sample.Width,
math.Max(sample.Delta, math.Floor(s.ƒ(s, r))-1),
// TODO(beorn7): How to calculate delta correctly?
}
i++
goto inserted
}
r += c.Width
}
s.l = append(s.l, Sample{sample.Value, sample.Width, 0})
i++
inserted:
s.n += sample.Width
r += sample.Width
}
s.compress()
}
func (s *stream) count() int {
return int(s.n)
}
func (s *stream) query(q float64) float64 {
t := math.Ceil(q * s.n)
t += math.Ceil(s.ƒ(s, t) / 2)
p := s.l[0]
var r float64
for _, c := range s.l[1:] {
r += p.Width
if r+c.Width+c.Delta > t {
return p.Value
}
p = c
}
return p.Value
}
func (s *stream) compress() {
if len(s.l) < 2 {
return
}
x := s.l[len(s.l)-1]
xi := len(s.l) - 1
r := s.n - 1 - x.Width
for i := len(s.l) - 2; i >= 0; i-- {
c := s.l[i]
if c.Width+x.Width+x.Delta <= s.ƒ(s, r) {
x.Width += c.Width
s.l[xi] = x
// Remove element at i.
copy(s.l[i:], s.l[i+1:])
s.l = s.l[:len(s.l)-1]
xi -= 1
} else {
x = c
xi = i
}
r -= c.Width
}
}
func (s *stream) samples() Samples {
samples := make(Samples, len(s.l))
copy(samples, s.l)
return samples
}

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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)
}
}

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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]
}

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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)
}
}
}

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# Compiled Object files, Static and Dynamic libs (Shared Objects)
*.o
*.a
*.so
# Folders
_obj
_test
# Architecture specific extensions/prefixes
*.[568vq]
[568vq].out
*.cgo1.go
*.cgo2.c
_cgo_defun.c
_cgo_gotypes.go
_cgo_export.*
_testmain.go
*.exe
*.test
*.prof
bin/
coverage/

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language: go
go:
- 1.5.4
- 1.6.2
script:
- ./test

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# How to Contribute
CoreOS projects are [Apache 2.0 licensed](LICENSE) and accept contributions via
GitHub pull requests. This document outlines some of the conventions on
development workflow, commit message formatting, contact points and other
resources to make it easier to get your contribution accepted.
# Certificate of Origin
By contributing to this project you agree to the Developer Certificate of
Origin (DCO). This document was created by the Linux Kernel community and is a
simple statement that you, as a contributor, have the legal right to make the
contribution. See the [DCO](DCO) file for details.
# Email and Chat
The project currently uses the general CoreOS email list and IRC channel:
- Email: [coreos-dev](https://groups.google.com/forum/#!forum/coreos-dev)
- IRC: #[coreos](irc://irc.freenode.org:6667/#coreos) IRC channel on freenode.org
Please avoid emailing maintainers found in the MAINTAINERS file directly. They
are very busy and read the mailing lists.
## Getting Started
- Fork the repository on GitHub
- Read the [README](README.md) for build and test instructions
- Play with the project, submit bugs, submit patches!
## Contribution Flow
This is a rough outline of what a contributor's workflow looks like:
- Create a topic branch from where you want to base your work (usually master).
- Make commits of logical units.
- Make sure your commit messages are in the proper format (see below).
- Push your changes to a topic branch in your fork of the repository.
- Make sure the tests pass, and add any new tests as appropriate.
- Submit a pull request to the original repository.
Thanks for your contributions!
### Format of the Commit Message
We follow a rough convention for commit messages that is designed to answer two
questions: what changed and why. The subject line should feature the what and
the body of the commit should describe the why.
```
scripts: add the test-cluster command
this uses tmux to setup a test cluster that you can easily kill and
start for debugging.
Fixes #38
```
The format can be described more formally as follows:
```
<subsystem>: <what changed>
<BLANK LINE>
<why this change was made>
<BLANK LINE>
<footer>
```
The first line is the subject and should be no longer than 70 characters, the
second line is always blank, and other lines should be wrapped at 80 characters.
This allows the message to be easier to read on GitHub as well as in various
git tools.

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Developer Certificate of Origin
Version 1.1
Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
660 York Street, Suite 102,
San Francisco, CA 94110 USA
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
Developer's Certificate of Origin 1.1
By making a contribution to this project, I certify that:
(a) The contribution was created in whole or in part by me and I
have the right to submit it under the open source license
indicated in the file; or
(b) The contribution is based upon previous work that, to the best
of my knowledge, is covered under an appropriate open source
license and I have the right under that license to submit that
work with modifications, whether created in whole or in part
by me, under the same open source license (unless I am
permitted to submit under a different license), as indicated
in the file; or
(c) The contribution was provided directly to me by some other
person who certified (a), (b) or (c) and I have not modified
it.
(d) I understand and agree that this project and the contribution
are public and that a record of the contribution (including all
personal information I submit with it, including my sign-off) is
maintained indefinitely and may be redistributed consistent with
this project or the open source license(s) involved.

202
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Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
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outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
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"Source" form shall mean the preferred form for making modifications,
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"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
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"Contribution" shall mean any work of authorship, including
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to that Work or Derivative Works thereof, that is intentionally
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"Contributor" shall mean Licensor and any individual or Legal Entity
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2. Grant of Copyright License. Subject to the terms and conditions of
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or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
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meet the following conditions:
(a) You must give any other recipients of the Work or
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(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
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within a display generated by the Derivative Works, if and
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of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
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You may add Your own copyright statement to Your modifications and
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5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
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except as required for reasonable and customary use in describing the
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7. Disclaimer of Warranty. Unless required by applicable law or
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of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
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risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
replaced with your own identifying information. (Don't include
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Copyright {yyyy} {name of copyright owner}
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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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.

1
vendor/github.com/coreos/pkg/MAINTAINERS generated vendored Normal file
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Ed Rooth <ed.rooth@coreos.com> (@sym3tri)

5
vendor/github.com/coreos/pkg/NOTICE generated vendored Normal file
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CoreOS Project
Copyright 2014 CoreOS, Inc
This product includes software developed at CoreOS, Inc.
(http://www.coreos.com/).

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a collection of go utility packages
[![Build Status](https://travis-ci.org/coreos/pkg.png?branch=master)](https://travis-ci.org/coreos/pkg)
[![Godoc](http://img.shields.io/badge/godoc-reference-blue.svg?style=flat)](https://godoc.org/github.com/coreos/pkg)

3
vendor/github.com/coreos/pkg/build generated vendored Executable file
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#!/bin/bash -e
go build ./...

39
vendor/github.com/coreos/pkg/capnslog/README.md generated vendored Normal file
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# capnslog, the CoreOS logging package
There are far too many logging packages out there, with varying degrees of licenses, far too many features (colorization, all sorts of log frameworks) or are just a pain to use (lack of `Fatalln()`?).
capnslog provides a simple but consistent logging interface suitable for all kinds of projects.
### Design Principles
##### `package main` is the place where logging gets turned on and routed
A library should not touch log options, only generate log entries. Libraries are silent until main lets them speak.
##### All log options are runtime-configurable.
Still the job of `main` to expose these configurations. `main` may delegate this to, say, a configuration webhook, but does so explicitly.
##### There is one log object per package. It is registered under its repository and package name.
`main` activates logging for its repository and any dependency repositories it would also like to have output in its logstream. `main` also dictates at which level each subpackage logs.
##### There is *one* output stream, and it is an `io.Writer` composed with a formatter.
Splitting streams is probably not the job of your program, but rather, your log aggregation framework. If you must split output streams, again, `main` configures this and you can write a very simple two-output struct that satisfies io.Writer.
Fancy colorful formatting and JSON output are beyond the scope of a basic logging framework -- they're application/log-collector dependant. These are, at best, provided as options, but more likely, provided by your application.
##### Log objects are an interface
An object knows best how to print itself. Log objects can collect more interesting metadata if they wish, however, because text isn't going away anytime soon, they must all be marshalable to text. The simplest log object is a string, which returns itself. If you wish to do more fancy tricks for printing your log objects, see also JSON output -- introspect and write a formatter which can handle your advanced log interface. Making strings is the only thing guaranteed.
##### Log levels have specific meanings:
* Critical: Unrecoverable. Must fail.
* Error: Data has been lost, a request has failed for a bad reason, or a required resource has been lost
* Warning: (Hopefully) Temporary conditions that may cause errors, but may work fine. A replica disappearing (that may reconnect) is a warning.
* Notice: Normal, but important (uncommon) log information.
* Info: Normal, working log information, everything is fine, but helpful notices for auditing or common operations.
* Debug: Everything is still fine, but even common operations may be logged, and less helpful but more quantity of notices.
* Trace: Anything goes, from logging every function call as part of a common operation, to tracing execution of a query.

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// Copyright 2015 CoreOS, Inc.
//