Switch to an ewma fork that allows setting the warmup samples #
This commit is contained in:
Frank Denis
parent
9ec8a35468
commit
7956ba5b10
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@ -3,7 +3,7 @@ package main
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import (
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import (
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"sync"
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"sync"
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"github.com/VividCortex/ewma"
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"github.com/jedisct1/ewma"
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)
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)
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const (
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const (
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@ -15,8 +15,8 @@ import (
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"sync"
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"sync"
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"time"
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"time"
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"github.com/VividCortex/ewma"
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"github.com/jedisct1/dlog"
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"github.com/jedisct1/dlog"
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"github.com/jedisct1/ewma"
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clocksmith "github.com/jedisct1/go-clocksmith"
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clocksmith "github.com/jedisct1/go-clocksmith"
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stamps "github.com/jedisct1/go-dnsstamps"
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stamps "github.com/jedisct1/go-dnsstamps"
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"github.com/miekg/dns"
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"github.com/miekg/dns"
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@ -266,7 +266,7 @@ func (serversInfo *ServersInfo) estimatorUpdate() {
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partialSort = true
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partialSort = true
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} else if candidateRtt > 0 && candidateRtt >= (serversInfo.inner[0].rtt.Value()+serversInfo.inner[activeCount-1].rtt.Value())/2.0*4.0 {
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} else if candidateRtt > 0 && candidateRtt >= (serversInfo.inner[0].rtt.Value()+serversInfo.inner[activeCount-1].rtt.Value())/2.0*4.0 {
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if time.Since(serversInfo.inner[candidate].lastActionTS) > time.Duration(1*time.Minute) {
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if time.Since(serversInfo.inner[candidate].lastActionTS) > time.Duration(1*time.Minute) {
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serversInfo.inner[candidate].rtt.Add(candidateRtt/2.0)
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serversInfo.inner[candidate].rtt.Add(candidateRtt / 2.0)
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dlog.Debugf("Giving a new chance to candidate [%s], lowering its RTT from %d to %d (best: %d)", serversInfo.inner[candidate].Name, int(candidateRtt), int(serversInfo.inner[candidate].rtt.Value()), int(serversInfo.inner[0].rtt.Value()))
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dlog.Debugf("Giving a new chance to candidate [%s], lowering its RTT from %d to %d (best: %d)", serversInfo.inner[candidate].Name, int(candidateRtt), int(serversInfo.inner[candidate].rtt.Value()), int(serversInfo.inner[0].rtt.Value()))
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partialSort = true
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partialSort = true
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}
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}
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2
go.mod
2
go.mod
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@ -4,13 +4,13 @@ go 1.17
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require (
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require (
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github.com/BurntSushi/toml v1.0.0
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github.com/BurntSushi/toml v1.0.0
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github.com/VividCortex/ewma v1.2.0
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github.com/coreos/go-systemd v0.0.0-20191104093116-d3cd4ed1dbcf
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github.com/coreos/go-systemd v0.0.0-20191104093116-d3cd4ed1dbcf
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github.com/dchest/safefile v0.0.0-20151022103144-855e8d98f185
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github.com/dchest/safefile v0.0.0-20151022103144-855e8d98f185
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github.com/hashicorp/go-immutable-radix v1.3.1
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github.com/hashicorp/go-immutable-radix v1.3.1
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github.com/hashicorp/golang-lru v0.5.4
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github.com/hashicorp/golang-lru v0.5.4
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github.com/hectane/go-acl v0.0.0-20190604041725-da78bae5fc95
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github.com/hectane/go-acl v0.0.0-20190604041725-da78bae5fc95
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f
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github.com/jedisct1/ewma v1.2.1-0.20220220223311-a30af446ecb9
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868
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github.com/jedisct1/go-dnsstamps v0.0.0-20210810213811-61cc83d2a354
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github.com/jedisct1/go-dnsstamps v0.0.0-20210810213811-61cc83d2a354
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github.com/jedisct1/go-hpke-compact v0.0.0-20210930135406-0763750339f0
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github.com/jedisct1/go-hpke-compact v0.0.0-20210930135406-0763750339f0
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4
go.sum
4
go.sum
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@ -52,8 +52,6 @@ github.com/OneOfOne/xxhash v1.2.2/go.mod h1:HSdplMjZKSmBqAxg5vPj2TmRDmfkzw+cTzAE
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github.com/OpenPeeDeeP/depguard v1.0.1 h1:VlW4R6jmBIv3/u1JNlawEvJMM4J+dPORPaZasQee8Us=
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github.com/OpenPeeDeeP/depguard v1.0.1 h1:VlW4R6jmBIv3/u1JNlawEvJMM4J+dPORPaZasQee8Us=
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github.com/OpenPeeDeeP/depguard v1.0.1/go.mod h1:xsIw86fROiiwelg+jB2uM9PiKihMMmUx/1V+TNhjQvM=
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github.com/OpenPeeDeeP/depguard v1.0.1/go.mod h1:xsIw86fROiiwelg+jB2uM9PiKihMMmUx/1V+TNhjQvM=
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github.com/StackExchange/wmi v0.0.0-20190523213315-cbe66965904d/go.mod h1:3eOhrUMpNV+6aFIbp5/iudMxNCF27Vw2OZgy4xEx0Fg=
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github.com/StackExchange/wmi v0.0.0-20190523213315-cbe66965904d/go.mod h1:3eOhrUMpNV+6aFIbp5/iudMxNCF27Vw2OZgy4xEx0Fg=
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github.com/VividCortex/ewma v1.2.0 h1:f58SaIzcDXrSy3kWaHNvuJgJ3Nmz59Zji6XoJR/q1ow=
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github.com/VividCortex/ewma v1.2.0/go.mod h1:nz4BbCtbLyFDeC9SUHbtcT5644juEuWfUAUnGx7j5l4=
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github.com/alecthomas/template v0.0.0-20160405071501-a0175ee3bccc/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc=
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github.com/alecthomas/template v0.0.0-20160405071501-a0175ee3bccc/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc=
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github.com/alecthomas/template v0.0.0-20190718012654-fb15b899a751/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc=
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github.com/alecthomas/template v0.0.0-20190718012654-fb15b899a751/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc=
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github.com/alecthomas/units v0.0.0-20151022065526-2efee857e7cf/go.mod h1:ybxpYRFXyAe+OPACYpWeL0wqObRcbAqCMya13uyzqw0=
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github.com/alecthomas/units v0.0.0-20151022065526-2efee857e7cf/go.mod h1:ybxpYRFXyAe+OPACYpWeL0wqObRcbAqCMya13uyzqw0=
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@ -353,6 +351,8 @@ github.com/inconshreveable/mousetrap v1.0.0 h1:Z8tu5sraLXCXIcARxBp/8cbvlwVa7Z1NH
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github.com/inconshreveable/mousetrap v1.0.0/go.mod h1:PxqpIevigyE2G7u3NXJIT2ANytuPF1OarO4DADm73n8=
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github.com/inconshreveable/mousetrap v1.0.0/go.mod h1:PxqpIevigyE2G7u3NXJIT2ANytuPF1OarO4DADm73n8=
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f h1:XICcphytniQKdtd4FGrK0b1ERzS7FBvFtVUCReSppmU=
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f h1:XICcphytniQKdtd4FGrK0b1ERzS7FBvFtVUCReSppmU=
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f/go.mod h1:35aII3PkLMvmc8daWy0vcZXDU+a40lJczHHTFRJmnvw=
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github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f/go.mod h1:35aII3PkLMvmc8daWy0vcZXDU+a40lJczHHTFRJmnvw=
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github.com/jedisct1/ewma v1.2.1-0.20220220223311-a30af446ecb9 h1:U5QPCoM1KkMJ9RfEfP0joKNwwwIHG1oP9RzjvQTuh98=
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github.com/jedisct1/ewma v1.2.1-0.20220220223311-a30af446ecb9/go.mod h1:qCWdft6DX9wxyNsUS+sxS44UkxE7eQnNtBttTWoW0cU=
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868 h1:QZ79mRbNwYYYmiVjyv+X0NKgYE6nyN1yo3gtEFdzpiE=
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868 h1:QZ79mRbNwYYYmiVjyv+X0NKgYE6nyN1yo3gtEFdzpiE=
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868/go.mod h1:SAINchklztk2jcLWJ4bpNF4KnwDUSUTX+cJbspWC2Rw=
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github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868/go.mod h1:SAINchklztk2jcLWJ4bpNF4KnwDUSUTX+cJbspWC2Rw=
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github.com/jedisct1/go-dnsstamps v0.0.0-20210810213811-61cc83d2a354 h1:sIB9mDh2spQdh95jeXF2h9uSNtObbehD0YbDCzmqbM8=
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github.com/jedisct1/go-dnsstamps v0.0.0-20210810213811-61cc83d2a354 h1:sIB9mDh2spQdh95jeXF2h9uSNtObbehD0YbDCzmqbM8=
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@ -1,3 +0,0 @@
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.DS_Store
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.*.sw?
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/coverage.txt
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@ -1,3 +0,0 @@
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{
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"settingsInheritedFrom": "VividCortex/whitesource-config@master"
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}
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@ -1,21 +0,0 @@
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The MIT License
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Copyright (c) 2013 VividCortex
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
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THE SOFTWARE.
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@ -1,145 +0,0 @@
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# EWMA
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[](https://godoc.org/github.com/VividCortex/ewma)
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[](https://codecov.io/gh/VividCortex/ewma)
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This repo provides Exponentially Weighted Moving Average algorithms, or EWMAs for short, [based on our
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Quantifying Abnormal Behavior talk](https://vividcortex.com/blog/2013/07/23/a-fast-go-library-for-exponential-moving-averages/).
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### Exponentially Weighted Moving Average
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An exponentially weighted moving average is a way to continuously compute a type of
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average for a series of numbers, as the numbers arrive. After a value in the series is
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added to the average, its weight in the average decreases exponentially over time. This
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biases the average towards more recent data. EWMAs are useful for several reasons, chiefly
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their inexpensive computational and memory cost, as well as the fact that they represent
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the recent central tendency of the series of values.
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The EWMA algorithm requires a decay factor, alpha. The larger the alpha, the more the average
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is biased towards recent history. The alpha must be between 0 and 1, and is typically
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a fairly small number, such as 0.04. We will discuss the choice of alpha later.
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The algorithm works thus, in pseudocode:
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1. Multiply the next number in the series by alpha.
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2. Multiply the current value of the average by 1 minus alpha.
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3. Add the result of steps 1 and 2, and store it as the new current value of the average.
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4. Repeat for each number in the series.
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There are special-case behaviors for how to initialize the current value, and these vary
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between implementations. One approach is to start with the first value in the series;
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another is to average the first 10 or so values in the series using an arithmetic average,
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and then begin the incremental updating of the average. Each method has pros and cons.
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It may help to look at it pictorially. Suppose the series has five numbers, and we choose
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alpha to be 0.50 for simplicity. Here's the series, with numbers in the neighborhood of 300.
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Now let's take the moving average of those numbers. First we set the average to the value
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of the first number.
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Next we multiply the next number by alpha, multiply the current value by 1-alpha, and add
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them to generate a new value.
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This continues until we are done.
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Notice how each of the values in the series decays by half each time a new value
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is added, and the top of the bars in the lower portion of the image represents the
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size of the moving average. It is a smoothed, or low-pass, average of the original
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series.
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For further reading, see [Exponentially weighted moving average](http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) on wikipedia.
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### Choosing Alpha
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Consider a fixed-size sliding-window moving average (not an exponentially weighted moving average)
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that averages over the previous N samples. What is the average age of each sample? It is N/2.
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Now suppose that you wish to construct a EWMA whose samples have the same average age. The formula
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to compute the alpha required for this is: alpha = 2/(N+1). Proof is in the book
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"Production and Operations Analysis" by Steven Nahmias.
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So, for example, if you have a time-series with samples once per second, and you want to get the
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moving average over the previous minute, you should use an alpha of .032786885. This, by the way,
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is the constant alpha used for this repository's SimpleEWMA.
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### Implementations
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This repository contains two implementations of the EWMA algorithm, with different properties.
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The implementations all conform to the MovingAverage interface, and the constructor returns
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that type.
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Current implementations assume an implicit time interval of 1.0 between every sample added.
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That is, the passage of time is treated as though it's the same as the arrival of samples.
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If you need time-based decay when samples are not arriving precisely at set intervals, then
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this package will not support your needs at present.
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#### SimpleEWMA
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A SimpleEWMA is designed for low CPU and memory consumption. It **will** have different behavior than the VariableEWMA
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for multiple reasons. It has no warm-up period and it uses a constant
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decay. These properties let it use less memory. It will also behave
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differently when it's equal to zero, which is assumed to mean
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uninitialized, so if a value is likely to actually become zero over time,
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then any non-zero value will cause a sharp jump instead of a small change.
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#### VariableEWMA
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Unlike SimpleEWMA, this supports a custom age which must be stored, and thus uses more memory.
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It also has a "warmup" time when you start adding values to it. It will report a value of 0.0
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until you have added the required number of samples to it. It uses some memory to store the
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number of samples added to it. As a result it uses a little over twice the memory of SimpleEWMA.
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## Usage
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### API Documentation
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View the GoDoc generated documentation [here](http://godoc.org/github.com/VividCortex/ewma).
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```go
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package main
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import "github.com/VividCortex/ewma"
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func main() {
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samples := [100]float64{
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4599, 5711, 4746, 4621, 5037, 4218, 4925, 4281, 5207, 5203, 5594, 5149,
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}
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e := ewma.NewMovingAverage() //=> Returns a SimpleEWMA if called without params
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a := ewma.NewMovingAverage(5) //=> returns a VariableEWMA with a decay of 2 / (5 + 1)
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for _, f := range samples {
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e.Add(f)
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a.Add(f)
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}
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e.Value() //=> 13.577404704631077
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a.Value() //=> 1.5806140565521463e-12
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}
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```
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## Contributing
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We only accept pull requests for minor fixes or improvements. This includes:
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* Small bug fixes
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* Typos
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* Documentation or comments
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Please open issues to discuss new features. Pull requests for new features will be rejected,
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so we recommend forking the repository and making changes in your fork for your use case.
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## License
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|
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|
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This repository is Copyright (c) 2013 VividCortex, Inc. All rights reserved.
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It is licensed under the MIT license. Please see the LICENSE file for applicable license terms.
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@ -1,6 +0,0 @@
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coverage:
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||||||
status:
|
|
||||||
project:
|
|
||||||
default:
|
|
||||||
threshold: 15%
|
|
||||||
patch: off
|
|
||||||
|
|
@ -1,126 +0,0 @@
|
||||||
// Package ewma implements exponentially weighted moving averages.
|
|
||||||
package ewma
|
|
||||||
|
|
||||||
// Copyright (c) 2013 VividCortex, Inc. All rights reserved.
|
|
||||||
// Please see the LICENSE file for applicable license terms.
|
|
||||||
|
|
||||||
const (
|
|
||||||
// By default, we average over a one-minute period, which means the average
|
|
||||||
// age of the metrics in the period is 30 seconds.
|
|
||||||
AVG_METRIC_AGE float64 = 30.0
|
|
||||||
|
|
||||||
// The formula for computing the decay factor from the average age comes
|
|
||||||
// from "Production and Operations Analysis" by Steven Nahmias.
|
|
||||||
DECAY float64 = 2 / (float64(AVG_METRIC_AGE) + 1)
|
|
||||||
|
|
||||||
// For best results, the moving average should not be initialized to the
|
|
||||||
// samples it sees immediately. The book "Production and Operations
|
|
||||||
// Analysis" by Steven Nahmias suggests initializing the moving average to
|
|
||||||
// the mean of the first 10 samples. Until the VariableEwma has seen this
|
|
||||||
// many samples, it is not "ready" to be queried for the value of the
|
|
||||||
// moving average. This adds some memory cost.
|
|
||||||
WARMUP_SAMPLES uint8 = 10
|
|
||||||
)
|
|
||||||
|
|
||||||
// MovingAverage is the interface that computes a moving average over a time-
|
|
||||||
// series stream of numbers. The average may be over a window or exponentially
|
|
||||||
// decaying.
|
|
||||||
type MovingAverage interface {
|
|
||||||
Add(float64)
|
|
||||||
Value() float64
|
|
||||||
Set(float64)
|
|
||||||
}
|
|
||||||
|
|
||||||
// NewMovingAverage constructs a MovingAverage that computes an average with the
|
|
||||||
// desired characteristics in the moving window or exponential decay. If no
|
|
||||||
// age is given, it constructs a default exponentially weighted implementation
|
|
||||||
// that consumes minimal memory. The age is related to the decay factor alpha
|
|
||||||
// by the formula given for the DECAY constant. It signifies the average age
|
|
||||||
// of the samples as time goes to infinity.
|
|
||||||
func NewMovingAverage(age ...float64) MovingAverage {
|
|
||||||
if len(age) == 0 || age[0] == AVG_METRIC_AGE {
|
|
||||||
return new(SimpleEWMA)
|
|
||||||
}
|
|
||||||
return &VariableEWMA{
|
|
||||||
decay: 2 / (age[0] + 1),
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// A SimpleEWMA represents the exponentially weighted moving average of a
|
|
||||||
// series of numbers. It WILL have different behavior than the VariableEWMA
|
|
||||||
// for multiple reasons. It has no warm-up period and it uses a constant
|
|
||||||
// decay. These properties let it use less memory. It will also behave
|
|
||||||
// differently when it's equal to zero, which is assumed to mean
|
|
||||||
// uninitialized, so if a value is likely to actually become zero over time,
|
|
||||||
// then any non-zero value will cause a sharp jump instead of a small change.
|
|
||||||
// However, note that this takes a long time, and the value may just
|
|
||||||
// decays to a stable value that's close to zero, but which won't be mistaken
|
|
||||||
// for uninitialized. See http://play.golang.org/p/litxBDr_RC for example.
|
|
||||||
type SimpleEWMA struct {
|
|
||||||
// The current value of the average. After adding with Add(), this is
|
|
||||||
// updated to reflect the average of all values seen thus far.
|
|
||||||
value float64
|
|
||||||
}
|
|
||||||
|
|
||||||
// Add adds a value to the series and updates the moving average.
|
|
||||||
func (e *SimpleEWMA) Add(value float64) {
|
|
||||||
if e.value == 0 { // this is a proxy for "uninitialized"
|
|
||||||
e.value = value
|
|
||||||
} else {
|
|
||||||
e.value = (value * DECAY) + (e.value * (1 - DECAY))
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Value returns the current value of the moving average.
|
|
||||||
func (e *SimpleEWMA) Value() float64 {
|
|
||||||
return e.value
|
|
||||||
}
|
|
||||||
|
|
||||||
// Set sets the EWMA's value.
|
|
||||||
func (e *SimpleEWMA) Set(value float64) {
|
|
||||||
e.value = value
|
|
||||||
}
|
|
||||||
|
|
||||||
// VariableEWMA represents the exponentially weighted moving average of a series of
|
|
||||||
// numbers. Unlike SimpleEWMA, it supports a custom age, and thus uses more memory.
|
|
||||||
type VariableEWMA struct {
|
|
||||||
// The multiplier factor by which the previous samples decay.
|
|
||||||
decay float64
|
|
||||||
// The current value of the average.
|
|
||||||
value float64
|
|
||||||
// The number of samples added to this instance.
|
|
||||||
count uint8
|
|
||||||
}
|
|
||||||
|
|
||||||
// Add adds a value to the series and updates the moving average.
|
|
||||||
func (e *VariableEWMA) Add(value float64) {
|
|
||||||
switch {
|
|
||||||
case e.count < WARMUP_SAMPLES:
|
|
||||||
e.count++
|
|
||||||
e.value += value
|
|
||||||
case e.count == WARMUP_SAMPLES:
|
|
||||||
e.count++
|
|
||||||
e.value = e.value / float64(WARMUP_SAMPLES)
|
|
||||||
e.value = (value * e.decay) + (e.value * (1 - e.decay))
|
|
||||||
default:
|
|
||||||
e.value = (value * e.decay) + (e.value * (1 - e.decay))
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Value returns the current value of the average, or 0.0 if the series hasn't
|
|
||||||
// warmed up yet.
|
|
||||||
func (e *VariableEWMA) Value() float64 {
|
|
||||||
if e.count <= WARMUP_SAMPLES {
|
|
||||||
return 0.0
|
|
||||||
}
|
|
||||||
|
|
||||||
return e.value
|
|
||||||
}
|
|
||||||
|
|
||||||
// Set sets the EWMA's value.
|
|
||||||
func (e *VariableEWMA) Set(value float64) {
|
|
||||||
e.value = value
|
|
||||||
if e.count <= WARMUP_SAMPLES {
|
|
||||||
e.count = WARMUP_SAMPLES + 1
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
@ -14,9 +14,6 @@ github.com/Masterminds/semver
|
||||||
# github.com/OpenPeeDeeP/depguard v1.0.1
|
# github.com/OpenPeeDeeP/depguard v1.0.1
|
||||||
## explicit; go 1.13
|
## explicit; go 1.13
|
||||||
github.com/OpenPeeDeeP/depguard
|
github.com/OpenPeeDeeP/depguard
|
||||||
# github.com/VividCortex/ewma v1.2.0
|
|
||||||
## explicit; go 1.12
|
|
||||||
github.com/VividCortex/ewma
|
|
||||||
# github.com/alexkohler/prealloc v1.0.0
|
# github.com/alexkohler/prealloc v1.0.0
|
||||||
## explicit; go 1.15
|
## explicit; go 1.15
|
||||||
github.com/alexkohler/prealloc/pkg
|
github.com/alexkohler/prealloc/pkg
|
||||||
|
|
@ -251,6 +248,9 @@ github.com/inconshreveable/mousetrap
|
||||||
# github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f
|
# github.com/jedisct1/dlog v0.0.0-20210927135244-3381aa132e7f
|
||||||
## explicit; go 1.17
|
## explicit; go 1.17
|
||||||
github.com/jedisct1/dlog
|
github.com/jedisct1/dlog
|
||||||
|
# github.com/jedisct1/ewma v1.2.1-0.20220220223311-a30af446ecb9
|
||||||
|
## explicit; go 1.12
|
||||||
|
github.com/jedisct1/ewma
|
||||||
# github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868
|
# github.com/jedisct1/go-clocksmith v0.0.0-20210101121932-da382b963868
|
||||||
## explicit
|
## explicit
|
||||||
github.com/jedisct1/go-clocksmith
|
github.com/jedisct1/go-clocksmith
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue