1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
|
// Copyright 2022 The OpenZipkin Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// 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.
package zipkin
import (
"fmt"
"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
// NeverSample will always return false. If used by a service it will not allow
// the service to start traces but will still allow the service to participate
// in traces started upstream.
func NeverSample(_ uint64) bool { return false }
// AlwaysSample will always return true. If used by a service it will always start
// traces if no upstream trace has been propagated. If an incoming upstream trace
// is not sampled the service will adhere to this and only propagate the context.
func AlwaysSample(_ uint64) bool { return true }
// NewModuloSampler provides a generic type Sampler.
func NewModuloSampler(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, error) {
if rate == 0.0 {
return NeverSample, nil
}
if rate == 1.0 {
return AlwaysSample, nil
}
if rate < 0.0001 || rate > 1 {
return nil, fmt.Errorf("rate should be 0.0 or between 0.0001 and 1: was %f", rate)
}
var (
boundary = int64(rate * 10000)
usalt = uint64(salt)
)
return func(id uint64) bool {
return int64(math.Abs(float64(id^usalt)))%10000 < boundary
}, nil
}
// 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, error) {
if rate == 0.0 {
return NeverSample, nil
}
if rate == 1.0 {
return AlwaysSample, nil
}
if rate < 0.01 || rate > 1 {
return nil, fmt.Errorf("rate should be 0.0 or between 0.01 and 1: was %f", rate)
}
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()
result := decisions[i]
i++
if i == 100 {
i = 0
}
mtx.Unlock()
return result
}, nil
}
/**
* 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
}
|
