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ࡾ༔հ / Pepabo R&D Institute, GMO Pepabo, Inc.
2018.11.25 Go Conference 2018 Autumn
ϑΟʔυόοΫ੍ޚʹΑΔ
Goroutineىಈͷ࠷దԽ
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ϓϦϯγύϧΤϯδχΞ
ࡾ༔հ!NPOPDISPNFHBOF
(.0ϖύϘגࣜձࣾϖύϘݚڀॴ
IUUQTCMPHNPOPDISPNFHBOFDPN
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3
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1. ฒߦॲཧͱνϡʔχϯάʹ͓͚Δ՝
2. KaburayaΞʔΩςΫνϟ
3. Goݴޠͷద༻
4. ධՁ
5. ·ͱΊ
4
࣍
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1.
ฒߦॲཧͱνϡʔχϯάʹ͓͚Δ՝
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ΦϨΰϧʔνϯͰߦ͘ʂ
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• ϚϧνίΞ࣌ʹ͓͍ͯɼ୯ҰͷϚγϯͰॲཧੑೳΛ࠷େԽ͢ΔͨΊॲཧͷฒ
ߦԽ͕ߦΘΕΔɽ
• ࠷దͳฒߦΛٻΊ͍ͨɽ
• ϊϯϒϩοΩϯάͳॲཧํࣜͰ࠷దͳฒߦCPUίΞͱ͍͠
• ϒϩοΩϯά͕Ұ෦Ͱൃੜ͢Δ߹CPUίΞҎ্͕࠷దʹͳΔ
߹͕ଟ͍
7
ฒߦॲཧͱ࠷దͳฒߦ
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• ϒϩοΩϯά͕ൃੜ͢Δঢ়گʹ͓͍ͯɼ࠷దͳฒߦͷܾఆґવͱͯ͠։
ൃऀͷܦݧͱಓͳνϡʔχϯάʹґଘ
• nginx, unicornͷworker_processesɼSidekiqͷconcurrencyɼGoroutineͷ
ىಈͳͲ
• ࣮ߦ͞ΕΔॲཧͷಛੑͱϥϯλΠϜεέδϡʔϦϯάͷಛੑΛߟྀͨ͠
νϡʔχϯά͕ඞཁ
• ͜ΕΒΛશͯߟྀͨ͠ීวతͳνϡʔχϯάख๏ͷൃݟࠔ
8
࠷దͳฒߦΛٻΊΔ
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• దͳࢦඪ͕બఆͰ͖Ε୯७ͳࢦඪͰޮՌ͕͋Δ
• Ծαʔόͷ༧ଌతΦʔτεέʔϦϯά (*1)ͰɼΞϓϦέʔγϣϯॲཧ
༰Ͱͳ͘ɼӡ༻ܦݧ͔Βಘͨαʔό୯ҐͷεϧʔϓοτΛࢦඪͱͨ͠ɽ
• ʮશࣗಈύϥϝʔλνϡʔχϯά͞Μʯ(*2)ͰϝτϦΫεΛҰʹߜΓε
ϧʔϓοτ͕࠷େԽ͢ΔΛ୳ࡧతʹٻΊͨɽ
9
ैདྷख๏
*1. ࡾ ༔հ, দຊ ྄հ, ྗ ݈࣍, ܀ྛ ݈ଠ, ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά, FIT 2018 ୈ17ճใ
Պֶٕज़ϑΥʔϥϜ, CL-002, Sep 2018.
*2. https://github.com/mirakui/tuningsan
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• ༧ଌతɼ୳ࡧతͳํࣜWebαʔόʔϓϩηεͷΑ͏ʹظՔಈ͢Δ߹ʹ
ޮՌత͕ͩɼCLIͷΑ͏ͳظՔಈͰଈ࣌݁Ռ͕ٻΊΒΕΔ߹ʹద༻͕͠
͍
10
ैདྷख๏
• ࢦඪͷݶఆʹՃ͑ɼԠత͔ͭޡ͕ࠩগͳ͘ࢦඪै͢Δख๏͕ٻΊΒΕΔ
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2.
KaburayaΞʔΩςΫνϟ
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• ॲཧϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡
Ԡత͔ͭܧଓతʹɼฒߦΛ࠷దԽ͢Δ͜ͱՄೳ͔ʁ
12
ຊݚڀͷʮ͍ʯ
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• ॲཧϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡Ԡత͔ͭܧଓ
తʹɼฒߦΛ࠷దԽ͢Δ
• 1. ॲཧϥϯλΠϜΛϒϥοΫϘοΫεͱͯ͠ѻ͍ɼܧଓతʹಈ࡞Λ੍ޚ͢
ΔΈͰ͋ΔϑΟʔυόοΫ੍ޚΛ༻͍ͯ࠷దͳฒߦΛٻΊΔ
• 2. ฒߦͷ੍ޚʹηϚϑΥΛ༻͍Δ
• Kaburaya is “ధ”. It is japanese arrow with a whistle.
13
Kaburaya ΞʔΩςΫνϟ
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• ඪͱग़ྗͷϑΟʔυόοΫͷޡࠩΛ༻͍ͯγεςϜͷೖྗΛܾఆ͠ɼग़ྗ
Λඪʹ͚ۙͮΔ
• ภࠩΛॲཧ͢Δ੍ޚثɼͦͯ͠ೖྗͱग़ྗʹԿΛ༻͍Δ͔ͷઃܭ͕ॏཁ
14
ϑΟʔυόοΫ੍ޚ
੍ޚث γεςϜ
ೖྗ ग़ྗ
ภࠩ
ϑΟʔυόοΫ
ඪ
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• ඪͱͷࠩΛޮతʹղফ͢ΔͨΊͷݹయత੍ޚख๏
• ൺྫ੍ޚ(P)ɼੵ੍ޚ(I)ɼඍ੍ޚ(D)ͷΛೖྗͱ͢Δ
15
PID੍ޚ(1/2)
੍ޚث
ภࠩ
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% Kd
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r
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e
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ϑΟʔυόοΫ
ೖྗ ग़ྗ
Slide 16
Slide 16 text
• ൺྫ੍ޚ(P)ภࠩʹൺྫ͢Δɽภ͕ࠩେ͖͍߹ʹେ͖͘मਖ਼ɽ
• ੵ੍ޚ(I)ภࠩͷ૯Λ༻͍ΔɽPͷཹภࠩΛղফ͢Δɽ
• ඍ੍ޚ(D)ภࠩͷඍΛ༻͍Δɽ֎ཚʹΑΔٸมಈʹػහʹରॲ͢Δɽ
16
PID੍ޚ(2/2)
੍ޚث
ภࠩ
ඪ 1 Kpe(t)
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* Ki
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% Kd
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γεςϜ
ϑΟʔυόοΫ
ೖྗ ग़ྗ
Slide 17
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1. ॳظ͔ΒϫʔΧʔΛม͑ͳ͍FixController (ൺֱ༻)
2. CPU༻100%Λඪͱ͠ɼෆͨ͠߹ workerΛ1ͣͭ૿Ճͤ͞Δ SimpleController
3. CPU༻100%Λඪͱ͠ɼඪͱͷࠩͷKഒΛՃ͑Δ PController(ൺྫ੍ޚ)
4. 3.ͷCPU༻ͷඪΛ90%ͱͨ͠ͷ
5. ۙ3؍ଌͷฏۉΛඪͱͯ͠3؍ଌ͝ͱʹPControllerͷඪΛมԽͤ͞ΔDynamicController
6. ҰఆظؒͷCPU༻ͷඪ४ภࠩΛͱͬͯͦΕ͕ҰఆͷҎԼͩͬͨΒ҆ఆͨ͠ͱΈͳͯ͠ɼworkerΛݮΒ͍ͯ͠
͘StabilityController
7. CPU༻Ͱͳͯ͘CPU༻ͷมԽΛݩʹ੍ޚ͢ΔRateController
8. 5.ͷDynamicControllerΛݩʹఆظͰͳ͘େ͖ͳมಈ͝ͱʹඪΛݟ͢ํࣜ
9. 8.ͷDynamicControllerΛݩʹఆৗ࣌ͷෆཁͳworkerΛݮ͢Δํࣜ
10. 9.ͷDynamicControllerΛݩʹมಈͷਫ਼ͱΛ্ͤ͞ΔͨΊʹੵඍ੍ޚΛಋೖ͢Δํࣜ
17
࠷దͳ੍ޚثΛٻΊͯ
Slide 18
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• ઃܭ੍ͨ͠ޚثΛฒߦԽ͞ΕͨλεΫΛܭ
ࢉࢿݯ্ݶ·Ͱฒྻॲཧ͢Δ؆қతͳγϛϡ
ϨʔλͰධՁ
18
؆қධՁ
• λεΫಛੑʹΑͬͯCPU্ݶҟͳΔ
• ࠷దͳฒߦ࣮ߦ࣌ͷΈஅͰ͖ΔͨΊ
CPU༻͕ҰఆͱͳΔΑ͏ʹ૿ݮ͕ඞཁ
• CPU༻ͷ૿ݮΛܖػʹඪΛݟ͢
͜ͱ͕ैͷ্ʹޮՌ͋Γ
Slide 19
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19
Kaburaya: DynamicTargetController
ภࠩ
ඪ
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
(PBQQ
ϑΟʔυόοΫ
4FNBQIPSF $166TBHF
มԽ
• ฒྻॲཧͷݶͱͳΔCPU༻Λ࣮ߦ࣌ʹଊ͑ΔͨΊඪΛมԽ͔Βߋ
৽͢Δೋॏϧʔϓ੍ޚثΛ࣮ɽ
• ଆͷϧʔϓฒߦΛೖྗɼCPU༻Λग़ྗͱ͢ΔPID੍ޚث
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• ༧ߘʮKaburaya: CPUෛՙʹԠͯ͡ܧଓతʹ্ݶΛ࠷దԽ͢ΔಈతηϚ
ϑΥʯͱͯ͠ୈ3ճWeb System Architectureݚڀձˏ౦ژ #wsaݚͰൃද
• https://blog.monochromegane.com/blog/2018/11/25/wsa_3_kaburaya/
• https://speakerdeck.com/monochromegane/wsa-3-kaburaya
20
༧ߘ
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3.
Goݴޠͷద༻
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• CPUόϯυͳॲཧͱͯ͠ϑΟϘφον(35)ͱ(10)ΛҰఆճٻΊΔॲཧ࣌ؒΛܭଌ
• CPUΛ100%͍ΔΑ͏ͳ߹ίΞҎ্ͷฒߦੑೳ্͠ͳ͍
• CPUʹ༨༟͕͋Δ߹ίΞҎ্ͷฒߦͰੑೳ্ͷ༨͕͋Δ
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• ϒϩοΩϯάॲཧͱͯ͠SleepNetworkΛҰఆճߦ͏ॲཧ࣌ؒΛܭଌ
• ඇৗʹ͍SleepͰίΞҎ্ͷฒߦͰੑೳ্ͷ༨͕͋Δ
• ωοτϫʔΫॲཧͰͪͷ͕͞ฒྻԽͷԸܙΛͳͨ͘͢Ίੑೳ্͕ฒߦʹґଘ
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• ϥϯλΠϜٖࣅతͳฒྻԽ͘͠ϚϧνίΞΛར༻͢ΔฒྻԽΛఏڙ͢Δ͕ɼδϣϒͷಛ
ੑʹΑͬͯɼλεΫॲཧ࣌ͷCPU༻CPU্ݶ͘͠ಛఆͷ࣌ͰมԽ͠ͳ͘ͳΔ
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• λεΫͷಛੑɼϥϯλΠϜͷಛੑʹΑͬͯ࠷దͳฒߦҟͳΔɽ
• ฒྻॲཧͷ݁ՌɼCPU༻͕CPU্ݶ·ͨҰఆͰۉߧ͢Δɽ
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ฒߦͷࢉग़
• KaburayaΞʔΩςΫνϟͷલఏʹԊ͏͜ͱ͔Βɼ੍ޚثͱͯ͠
DynamicTargetControllerΛར༻
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Goݴޠʹ͓͚ΔόοϑΝ͖νϟϯωϧΛ
ηϚϑΥͱ͢Δಉ࣮࣌ߦ੍ޚ 26
ฒߦͷ੍ޚ
func main() {
var wg sync.WaitGroup
sem := make(chan struct{}, 3)
for i := 0; i < 10; i++ {
wg.Add(1)
sem <- struct{}{}
go func() {
defer wg.Done()
defer func() { <-sem }()
task()
}()
}
wg.Wait()
close(sem)
}
• ηϚϑΥͷ্ݶ͕ಈతʹมߋՄೳͰ
͋Δ͜ͱ
• ௨ৗͷηϚϑΥͱಉ༷ʹPૢ࡞ʹ͓͍
͕ͯෛʹͳΔ߹ʹ࣮ߦ͕ϒϩοΫ
͞ΕΔ͜ͱ
• ͜ΕΒͷͷมߋ͕ΞτϛοΫʹߦΘ
ΕΔ͜ͱ
• ্ݶΛมߋՄೳͳόοϑΝ͖νϟ
ϯωϧ͕ඞཁ
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• ೖྗͱग़ྗͷೋͭͷνϟϯωϧͱΩϡʔΛͭߏମ
• Ωϡʔͷঢ়ଶʹԠͯ͡ೖྗͱग़ྗͷՄ൱Λ੍ޚ͢Δ
• Ref: https://github.com/npat-efault/musings/wiki/Elastic-channels
• Kaburaya༻్ͰΩϡʔͷΛར༻͢Δ͜ͱͳ͍ͷͰΧϯλͰ༻
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όοϑΝ্ݶΛมߋՄೳͳElasticChannel
DIBO DIBO
&MBTUJD$IBOOFM
2VFVF
&NQUZ*OPOMZ
'VMM0VUPOMZ
0UIFS*O0VU
$IBOHFMJNJU
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• Kaburaya optimize the number of goroutines by feedback control.
28
monochromegane/kaburaya
func main() {
sem := kaburaya.NewSem(100 * time.Millisecond)
var wg sync.WaitGroup
for i := 0; i < 10; i++ {
wg.Add(1)
sem.Wait()
go func() {
defer sem.Signal()
defer wg.Done()
task()
}()
}
wg.Wait()
sem.Stop()
}
Create semaphore and
specify time span for
update the semaphore and
CPU target.
Wait for decrement operation.
Increment operation.
Stop update the semaphore and CPU target.
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4.
ධՁ
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• ߴԽ(Speedup): ஞ࣮࣍ߦ࣌ؒͱฒྻ࣮ߦ࣌ؒͷൺ
• ࣮ߦޮ(Efficiency): ฒߦ͋ͨΓͷߴԽɽܭࢉࢿݯͷޮੑࢦඪɽ
30
࠷దͳฒߦͱ
$ ./score fib 40 64
[S] 41.20142734s
[1] 40.94367043s -> Speedup: 1.006301 Efficiency: 1.006301
[2] 21.138453817s -> Speedup: 1.949144 Efficiency: 0.974572
[4] 12.296148779s -> Speedup: 3.350764 Efficiency: 0.837691
[8] 12.371473761s -> Speedup: 3.330450 Efficiency: 0.416306
[16] 12.385120065s -> Speedup: 3.326686 Efficiency: 0.207918
• ͳ͓ɼKaburayaͰฒߦ͕૿ݮ͠ɼಛੑ্֎Εؚ͕·ΕΔ͜ͱ͔Βɼ࣮
ߦޮͷฒߦʹɼॲཧظؒͷฒߦͷதԝΛ༻͍Δ
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network(1) * 8192
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$ ./score network 1 8192 2> record.txt
[S] 18.906590961s
[1] 18.114622479s -> Speedup: 1.043723 Efficiency: 1.043723
[2] 8.311113276s -> Speedup: 2.274817 Efficiency: 1.137408
[4] 3.406973694s -> Speedup: 5.550793 Efficiency: 1.387698
[8] 1.690554271s -> Speedup: 11.186982 Efficiency: 1.398373
[16] 920.629201ms -> Speedup: 20.550000 Efficiency: 1.284375
[32] 664.033522ms -> Speedup: 28.472892 Efficiency: 0.889778
[64] 655.373022ms -> Speedup: 28.864122 Efficiency: 0.451002
[K] 738.116568ms -> Speedup: 25.617886 Efficiency: 0.711608
• K{p,i,d}=0.25ɼDuration=50ms
• CPU্ݶʹୡ͠ͳ͍λεΫʹରͯ͠ൺֱతଥͳฒߦͷ੍ޚ͕ߦ͑ͨɽ
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fib(40) * 64
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$ ./score fib 40 64 2> record.txt
[S] 42.498952641s
[1] 42.727265764s -> Speedup: 0.994640 Efficiency: 0.994640
[2] 21.084712153s -> Speedup: 2.015652 Efficiency: 1.007826
[4] 11.907186913s -> Speedup: 3.569161 Efficiency: 0.892290
[8] 12.128384712s -> Speedup: 3.504123 Efficiency: 0.438015
[16] 11.923092477s -> Speedup: 3.564371 Efficiency: 0.222773
[32] 11.911846969s -> Speedup: 3.567962 Efficiency: 0.111499
[64] 11.459039734s -> Speedup: 3.708701 Efficiency: 0.057948
[K] 13.368050152s -> Speedup: 3.179084 Efficiency: 0.635817
• K{p,i,d}=0.05ɼDuration=50ms
• CPU্ݶʹୡ͢ΔλεΫʹରͯ͠ൺֱతଥͳฒߦͷ੍ޚ͕ߦ͑ͨɽ
• λεΫྃ·ͰͷσΟϨΠʹΑͬͯηϚϑΥͷݮ͕ਐΈ͗͢Δ͋Γ
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fib(15) * 3276800
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$ ./score fib 15 3276800 2> record.txt
[S] 13.069192052s
[1] 15.275737367s -> Speedup: 0.855581 Efficiency: 0.855581
[2] 10.518883281s -> Speedup: 1.242537 Efficiency: 0.621268
[4] 6.452542685s -> Speedup: 2.025573 Efficiency: 0.506393
[8] 4.261144318s -> Speedup: 3.067120 Efficiency: 0.383390
[16] 4.173533581s -> Speedup: 3.131800 Efficiency: 0.195737
[32] 4.096993901s -> Speedup: 3.190674 Efficiency: 0.099709
[64] 4.111361424s -> Speedup: 3.179032 Efficiency: 0.049672
[K] 15.535130172s -> Speedup: 0.841262 Efficiency: 0.052579
• K{p,i,d}=0.01ɼDuration=50ms
• CPUෛՙͷগͳ͍େྔͷλεΫʹରͯ͠ߴԽɼ࣮ߦޮͷ྆໘Ͱࣦഊ
• ݱ࣮ͰElasticChannelͷWaitૢ࡞Ͱbuffer0ͷChannel͕ϘτϧωοΫ
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• ϑΟʔυόοΫ੍ޚҰൠʹର͢Δ՝
• ήΠϯ(K{p,i,d})ͷ࠷దΛٻΊΔඞཁ͕͋Δ
• σΟϨΠʹΑΔௐޡࠩΛݮΒ͢ඞཁ͕͋Δ
• ϑΟʔυόοΫ੍ޚͷϊϋΛ༗ޮ׆༻ͯ͠ղܾ͍ͨ͠
• ࣮ʹର͢Δ՝
• GoݴޠʹΑΔಈతηϚϑΥͷੑೳΛղܾ͢Δඞཁ͕͋Δ
• System V semaphoreͳͲϨΠϠ࣮Ͱղܾ͍ͨ͠
34
՝ͱରԠࢦ
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5.
·ͱΊ
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• ॲཧϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡Ԡత͔ͭܧଓ
తʹɼฒߦΛ࠷దԽ͢Δख๏ͱͯ͠ɼCPUෛՙʹԠͯ͡ܧଓతʹ্ݶΛ࠷
దԽ͢ΔKaburayaΞʔΩςΫνϟΛGoݴޠʹద༻
• ฒߦͷ૿Ճ͕ੑೳʹد༩͢ΔλεΫʹ͓͍ͯɼଥͳฒߦΛࢉग़Մೳ
• ࠓޙ՝Λղܾͯ͠ਫ਼ͱΛ্ͤ͞ɼݴޠΛ͑ͨ൚༻ੑΛࢦ͢
• Go
36
·ͱΊ
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