Allow dot in function name

This patch enables the use-case for multiple namepsaces by
allowing a dot to be used in the function name.

dep has been run to update OpenFaaS projects and also to
prune unused files.

Tested by doing a build.

Signed-off-by: Alex Ellis (OpenFaaS Ltd) <alexellis2@gmail.com>
This commit is contained in:
Alex Ellis (OpenFaaS Ltd)
2019-09-20 11:12:19 +01:00
committed by Alex Ellis
parent dc3c5fb9b3
commit 0a90125aba
1298 changed files with 86 additions and 629745 deletions

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*.test
*.prof

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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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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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// 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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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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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)
}
}
}