1 files changed, 0 insertions, 99 deletions
diff --git a/vendor/github.com/openzipkin/zipkin-go-opentracing/sample.go b/vendor/github.com/openzipkin/zipkin-go-opentracing/sample.go
deleted file mode 100644
index bb7ff0a53..000000000
--- a/vendor/github.com/openzipkin/zipkin-go-opentracing/sample.go
+++ /dev/null
@@ -1,99 +0,0 @@
-package zipkintracer
-
-import (
-	"math"
-	"math/rand"
-	"sync"
-	"time"
-)
-
-// Sampler functions return if a Zipkin span should be sampled, based on its
-// traceID.
-type Sampler func(id uint64) bool
-
-func neverSample(_ uint64) bool { return false }
-
-func alwaysSample(_ uint64) bool { return true }
-
-// ModuloSampler provides a typical OpenTracing type Sampler.
-func ModuloSampler(mod uint64) Sampler {
-	if mod < 2 {
-		return alwaysSample
-	}
-	return func(id uint64) bool {
-		return (id % mod) == 0
-	}
-}
-
-// NewBoundarySampler is appropriate for high-traffic instrumentation who
-// provision random trace ids, and make the sampling decision only once.
-// It defends against nodes in the cluster selecting exactly the same ids.
-func NewBoundarySampler(rate float64, salt int64) Sampler {
-	if rate <= 0 {
-		return neverSample
-	}
-	if rate >= 1.0 {
-		return alwaysSample
-	}
-	var (
-		boundary = int64(rate * 10000)
-		usalt    = uint64(salt)
-	)
-	return func(id uint64) bool {
-		return int64(math.Abs(float64(id^usalt)))%10000 < boundary
-	}
-}
-
-// NewCountingSampler is appropriate for low-traffic instrumentation or
-// those who do not provision random trace ids. It is not appropriate for
-// collectors as the sampling decision isn't idempotent (consistent based
-// on trace id).
-func NewCountingSampler(rate float64) Sampler {
-	if rate <= 0 {
-		return neverSample
-	}
-	if rate >= 1.0 {
-		return alwaysSample
-	}
-	var (
-		i         = 0
-		outOf100  = int(rate*100 + math.Copysign(0.5, rate*100)) // for rounding float to int conversion instead of truncation
-		decisions = randomBitSet(100, outOf100, rand.New(rand.NewSource(time.Now().UnixNano())))
-		mtx       = &sync.Mutex{}
-	)
-
-	return func(_ uint64) bool {
-		mtx.Lock()
-		defer mtx.Unlock()
-		result := decisions[i]
-		i++
-		if i == 100 {
-			i = 0
-		}
-		return result
-	}
-}
-
-/**
- * Reservoir sampling algorithm borrowed from Stack Overflow.
- *
- * http://stackoverflow.com/questions/12817946/generate-a-random-bitset-with-n-1s
- */
-func randomBitSet(size int, cardinality int, rnd *rand.Rand) []bool {
-	result := make([]bool, size)
-	chosen := make([]int, cardinality)
-	var i int
-	for i = 0; i < cardinality; i++ {
-		chosen[i] = i
-		result[i] = true
-	}
-	for ; i < size; i++ {
-		j := rnd.Intn(i + 1)
-		if j < cardinality {
-			result[chosen[j]] = false
-			result[i] = true
-			chosen[j] = i
-		}
-	}
-	return result
-}