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https://github.com/fnproject/fn.git
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206aa3c203
* update vendor directory, add go.opencensus.io * update imports * oops * s/opentracing/opencensus/ & remove prometheus / zipkin stuff & remove old stats * the dep train rides again * fix gin build * deps from last guy * start in on the agent metrics * she builds * remove tags for now, cardinality error is fussing. subscribe instead of register * update to patched version of opencensus to proceed for now TODO switch to a release * meh fix imports * println debug the bad boys * lace it with the tags * update deps again * fix all inconsistent cardinality errors * add our own logger * fix init * fix oom measure * remove bugged removal code * fix s3 measures * fix prom handler nil
108 lines
2.6 KiB
Go
108 lines
2.6 KiB
Go
package zipkin
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import (
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"fmt"
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"math"
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"math/rand"
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"sync"
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"time"
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)
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// Sampler functions return if a Zipkin span should be sampled, based on its
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// traceID.
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type Sampler func(id uint64) bool
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func neverSample(_ uint64) bool { return false }
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func alwaysSample(_ uint64) bool { return true }
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// NewModuloSampler provides a generic type Sampler.
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func NewModuloSampler(mod uint64) Sampler {
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if mod < 2 {
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return alwaysSample
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}
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return func(id uint64) bool {
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return (id % mod) == 0
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}
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}
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// NewBoundarySampler is appropriate for high-traffic instrumentation who
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// provision random trace ids, and make the sampling decision only once.
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// It defends against nodes in the cluster selecting exactly the same ids.
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func NewBoundarySampler(rate float64, salt int64) (Sampler, error) {
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if rate == 0.0 {
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return neverSample, nil
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}
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if rate == 1.0 {
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return alwaysSample, nil
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}
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if rate < 0.0001 || rate > 1 {
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return nil, fmt.Errorf("rate should be 0.0 or between 0.0001 and 1: was %f", rate)
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}
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var (
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boundary = int64(rate * 10000)
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usalt = uint64(salt)
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)
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return func(id uint64) bool {
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return int64(math.Abs(float64(id^usalt)))%10000 < boundary
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}, nil
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}
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// NewCountingSampler is appropriate for low-traffic instrumentation or
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// those who do not provision random trace ids. It is not appropriate for
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// collectors as the sampling decision isn't idempotent (consistent based
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// on trace id).
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func NewCountingSampler(rate float64) (Sampler, error) {
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if rate == 0.0 {
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return neverSample, nil
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}
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if rate == 1.0 {
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return alwaysSample, nil
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}
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if rate < 0.01 || rate > 1 {
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return nil, fmt.Errorf("rate should be 0.0 or between 0.01 and 1: was %f", rate)
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}
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var (
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i = 0
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outOf100 = int(rate*100 + math.Copysign(0.5, rate*100)) // for rounding float to int conversion instead of truncation
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decisions = randomBitSet(100, outOf100, rand.New(rand.NewSource(time.Now().UnixNano())))
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mtx = &sync.Mutex{}
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)
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return func(_ uint64) bool {
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mtx.Lock()
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result := decisions[i]
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i++
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if i == 100 {
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i = 0
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}
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mtx.Unlock()
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return result
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}, nil
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}
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/**
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* Reservoir sampling algorithm borrowed from Stack Overflow.
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*
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* http://stackoverflow.com/questions/12817946/generate-a-random-bitset-with-n-1s
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*/
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func randomBitSet(size int, cardinality int, rnd *rand.Rand) []bool {
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result := make([]bool, size)
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chosen := make([]int, cardinality)
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var i int
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for i = 0; i < cardinality; i++ {
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chosen[i] = i
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result[i] = true
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}
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for ; i < size; i++ {
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j := rnd.Intn(i + 1)
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if j < cardinality {
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result[chosen[j]] = false
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result[i] = true
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chosen[j] = i
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}
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}
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return result
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}
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