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E-book version still available
The dRuby Book is out of print, but...
スタンプラリー対象外です。Not eligible for stamp rally
2
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Agenda
Concurrency is everywhere
Introduction to Ractor
case study
3
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Agenda
Concurrency is everywhere
About me
Concurrency is everywhere
Introduction to Ractor
case study
4
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About me
Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer
I started programming 40 years ago
writing complex embedded systems for 30 years
system with concurrency
40年くらいコード書いてて、30年くらいは複雑なシステム書いてるよ
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Complex embedded systems
dRuby
in the real-world embedded systems.
ΩϠϊϯϝσΟΧϧγεςϜζגࣜձࣾ
ҩ༻ίϯϙʔωϯτٕज़։ൃηϯλʔɹؔকढ़ɹԂཾ
4FQ
ɹ
- ref. RubyKaigi takeout 2021
ミニマム構成の製品を使って若者をRubyistにした事例発表
About us
Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer
Tatsuya Sonokawa, Programmer
Miwa Fukaya, Tochigi RubyKaigi Content Committee Chair, Software
tester
ਓؒυοΫޙͷ࠶ݕࠪͰࡱͬͯΒͬͨࣸਅͰ͢ɻେਓͳͷͰɻ
9
404
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Complex embedded systems
dRuby
in the real-world embedded systems.
ΩϠϊϯϝσΟΧϧγεςϜζגࣜձࣾ
ҩ༻ίϯϙʔωϯτٕज़։ൃηϯλʔɹؔকढ़ɹԂཾ
4FQ
ɹ
- ref. RubyKaigi takeout 2021
X-ray CT image of my gallstones
About us
Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer
Tatsuya Sonokawa, Programmer
Miwa Fukaya, Tochigi RubyKaigi Content Committee Chair, Software
tester
ਓؒυοΫޙͷ࠶ݕࠪͰࡱͬͯΒͬͨࣸਅͰ͢ɻେਓͳͷͰɻ
9
404
Gallstones
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Complex embedded systems
Lots of processes, and explicit and implicit IPC
無数のプロセスとプロセス間通信がある。明示的だったり暗黙的だったりするよ
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Concurrency is everywhere
Concurrency exists at various levels of abstraction
そこら中で時間的にオーバーラップして実行してる。ということはつまり...
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Concurrency is everywhere
Sequential execution is a special case
むしろ、逐次実行は特殊なケースである
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RubyKaigi takeout 2020
Rinda
in the real-world embedded systems.
[email protected]
First printing from 2005 (Still available)
dRuby
ʹΑΔ
ؔকढ़ஶ
ࢄ
ɾ
Web
ϓϩάϥϛϯά
dRubyとRindaによる天文台のロボット望遠鏡（ガチ）の話
- 🤖🔭
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RubyKaigi takeout 2020
自律して動作する装置／センサー群を統合するシステム。抽象度が高い感じする
- Linda coordination model
software block diagram
1
psz117
X
f/2.5
20151207_yanagisawa.pdf ΑΓ
OAOWFC䛾እほ
2015/12/07 奛㷔塭伖㈨埻WS 16
X
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OpenMP
比較的低いレベルの例
13
int main(int argc, char *argv[])
{
int i;
#pragma omp parallel for
for (i = 0; i < 10000; ++i)
{
/* ... */
}
return 0;
}
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OpenMP
14
0...10000
int main(int argc, char *argv[])
{
int i;
#pragma omp parallel for
for (i = 0; i < 10000; ++i)
{
/* ... */
}
return 0;
}
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OpenMP
15
0...2000
2000...4000
4000...6000
6000...8000
8000...10000
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Agenda
Concurrency is everywhere
Introduction to Ractor
case study
16
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Ractor
parallel
avoid race condition
isolation
本当のスレッドと保護されたメモリー（オブジェクト）で
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Thread to Ractor
単純なケースではThreadのように使えるよ
18
>> t = Thread.new(*arg) {|*arg| ... }
...
>> t.value
>> r = Ractor.new(*arg) {|*arg| ... }
...
>> r.take
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Thread to Ractor
Ractorはtakeで値を取り出すのがちがうよ。
19
>> t = Thread.new(25) {|n| (1..n).inject(1, :*)}
...
>> t.value
=> 15511210043330985984000000
>> r = Ractor.new(25) {|n| (1..n).inject(1, :*)}
...
>> r.take
=> 15511210043330985984000000
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avoid race conditions
isolation
block scope
global variables
shareable object
競合状態を嫌ってRactor間をまたぐオブジェクトに強い制限がある
20
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isolation
block scope
Procだけど、外側の変数にはアクセスできないよ
21
>> hash = {'foo'=>'bar'}
>> r = Ractor.new {hash['foo']}
:271:in `new': can not isolate a Proc
because it accesses outer variables (i). (ArgumentError)
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isolation
global variable
グローバル変数にはアクセスできないぞ
22
>> $foo = 1
>> r = Ractor.new {$foo}
# terminated with exception
(report_on_exception is true):
(irb):11:in `block in ': can not access
global variables $foo from non-main Ractors
(Ractor::IsolationError)
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pass by value
deep copy
引数経由して渡せるよ。でもRactorをまたぐときに複製になる。dRubyみたいだ
23
>> hash = {'foo'=>'bar'}
>> r = Ractor.new(hash) {|it| it['foo'] = 'baz'}
...
>> r.take
=> "baz"
>> hash
=> {"foo"=>"bar"}
>> r = Ractor.new(hash) {|it| it.object_id}
...
>> [hash.object_id, r.take]
=> [2070360, 2006160]
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shareable
Ractor.make_shareable
make_shareableするとRactor間で共有されるぞ！でもfreezeされてしまうのだ
DRbUndumpedみたい
24
>> hash = {'foo'=>'bar'}
>> Ractor.make_shareable(hash)
>> Ractor.new(hash) {|it| it.object_id}.take
=> 221520
>> hash.object_id
=> 221520
>> hash['foo'] = 'baz'
(irb):16:in `': can't modify frozen Hash:
{"foo"=>"bar"} (FrozenError)
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can't make shareable
Queue, Monitor, TupleSpace
同期プリミティブは渡せない
25
>> Ractor.make_shareable(Queue.new)
:820:in `make_shareable': can not make
shareable object for #
(Ractor::Error)
>> Ractor.make_shareable(Monitor.new)
:820:in `make_shareable': can not make
shareable object for #
(Ractor::Error)
>> Ractor.make_shareable(Rinda::TupleSpace.new)
:820:in `make_shareable': can not make
shareable object for #
(Ractor::Error)
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shareable object
immutable
class/module
special object
Ractor
ENV
26
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Ractor has two queues.
takeしか使わなかった...
27
incoming queue
Ractor#send / Ractor.receive
outgoing queue
Ractor.yield / Ractor#take
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avoid race conditions
Using Ractor is easy, but writing for Ractor is hard.
straitjacket
拘束衣。オブジェクト指向プログラミングのように書くのはたいへんだと思う
28
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PG
Thought it wouldn't work, but it didn't
動かないと思うけど動かないもの
29
>> conn = PG.connect("postgres:///masaki")
>> Ractor.make_shareable(conn)
:820:in `make_shareable': can not make shareable
object for # (Ractor::Error)
>> Ractor.new { PG.connect("postgres:///masaki").exec("select 1") }
# terminated with exception
(report_on_exception is true):
/opt/homebrew/lib/ruby/gems/3.2.0/gems/pg-1.4.6/lib/pg/
connection.rb:70:in `conninfo_parse': ractor unsafe method called
from not main ractor (Ractor::UnsafeError)
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pp
Thought it would work, but it didn't
動きそうで動かなかったもの。私の使い方が悪いのかもしれないです。
30
>> Ractor.new {pp 1}
# terminated with exception
(report_on_exception is true):
:40:in `require': can not access
non-shareable objects in constant
Kernel::RUBYGEMS_ACTIVATION_MONITOR by non-main ractor.
(Ractor::IsolationError)
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a situation suitable for Ractor
Accelerate loops with parallelism
numeric calculation
in-memory database
さっき説明したOpenMP見たい領域が得意そう。
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There is an in-memory search engine
用意しておきました！
32
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Agenda
Concurrency is everywhere
Introduction to Ractor
case study
33
Slide 34
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RubyKaigi 2022
Create my own search engine.
[email protected]
ࣗ༻ͷݕࡧγεςϜΛ࡞Δ
- There is an in-memory search engine
dRubyについて話せなかったが、ポケモンカードの検索エンジンの話
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Pokémon
Trainer Energy
Pokémon TCG
Build a deck with 60 cards
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Pokémon TCG similar deck search
Heroku → Oracle Cloud Infrastructure
VM : Arm x 4 core
do not use containers
OCIに移行したので、コンテナを使うのもやめた
36
2023
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Weekly deck trend analysis
新機能！
37
case 1
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
38
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
39
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
m
C2
40
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Hierarchical clustering
Calculate distances for all combinations → mCn
m
C2
41
irb(main):001:0>
Array.new(6).combination(2).size
=> 15
irb(main):002:0>
Array.new(900).combination(2).size
=> 404550
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
42
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
43
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
44
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Hierarchical clustering
Calculate distances for all combinations
Merge closest node into cluster
45
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Hierarchical clustering
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nLgNgL-jK0iJz-QQnnng
Slide 47
Slide 47 text
Hierarchical clustering
0.0
0.1
0.2
0.3
0.4
vVkVkf-CfQ2Me-5wF5k5
pM2ySp-NqdVVy-SEXyyy
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nnHLnL-oJk1fq-QiLgLg
Slide 48
Slide 48 text
Hierarchical clustering
0.6
0.7
0.8
↑ Height
Slide 49
Slide 49 text
Weekly deck trend analysis
適当な高さで切るとデッキアーキテクチャごとのクラスタになる！
49
Slide 50
Slide 50 text
Pokémon TCG similar deck search 2023
50
card normalize map
data/uniq_*.txt
new deck
metadata
known deck
sqlite3
sqlite3
My MacBook
App
deck
similarity
Search
Initialize
Crowler
Web UI
@deck
@idf
@norm
@name
@id_norm
Make Vector
Update Deck
Build page
periodic task
検索エンジンを流用して
Slide 51
Slide 51 text
Weekly deck trend analysis
\
51
card normalize map
data/uniq_*.txt
new decks
known deck
sqlite3
sqlite3 Rakefile
deck
similarity
Initialize
Crowler
Clustering
@deck
@idf
@norm
@name
@id_norm
Make Vector
hierarchical
clustering
クラスタリング
n weeks
Slide 52
Slide 52 text
Weekly deck trend analysis
\
52
card normalize map
data/uniq_*.txt
new decks
known deck
sqlite3
sqlite3 Rakefile
deck
similarity
Initialize
Crowler
Clustering
@deck
@idf
@norm
@name
@id_norm
Make Vector
hierarchical
clustering
クラスタリング
n weeks
Slide 53
Slide 53 text
Ractor-ize
Using Ractor is easy, but writing for Ractor is hard.
Refactoring was very hard
Move calculation part to mix-in
めちゃめちゃリファクタリングした...
53
Slide 54
Slide 54 text
n週間分のRactorをつくるよ
54
def weekly_analyze(world, deck_and_date)
origin = Date.parse('2022-10-07') # ༵ۚ࢝·Γ
weekly = deck_and_date.sort_by {|x| x[1]}.chunk {|x|
(Date.parse(x[1]) - origin).to_i / 7
}
result = weekly.map {|_, decks|
{
'range' => Range.new(decks[0][1], decks[-1][1]),
'deck_count' => decks.size,
'cluster' => Cluster.new(world, decks.map {|x, d| x})
}
}
result.each do |x|
x['cluster'].join
end
result
end
Ractor.new
take
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55
2022-10-07 .. 2022-10-13
2022-10-14 .. 2022-10-20
2022-10-21 .. 2022-10-27
2022-11-07 .. 2022-11-03
2022-11-04 ... 2022-11-11
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map { Ractor.new }.map {|r| r.take }
Ractor起動！
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>> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
[ ]
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map { Ractor.new }.map {|r| r.take }
終わったRactorもいるし、動いてるRactorもいるぞ
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>> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
[ ]
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map { Ractor.new }.map {|r| r.take }
気にせずtakeで待ち合わせ！
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>> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
[ ]
>> result.map {|r| r.take}
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map { Ractor.new }.map {|r| r.take }
Linda本でもこういうイディオムあったような気がする
59
>> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
[ ]
>> result.map {|r| r.take}
[ ]
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28 Ractors, 8 cores (6 performance and 2 efficiency)
40%くらい速くなった。デッキ数が週ごとにばらつくため、多い週に律速した？
60
w/o Ractor
% /usr/bin/time -l rake
51.03 real 50.28 user 0.55 sys
Ractor
% /usr/bin/time -l rake
31.84 real 83.81 user 89.54 sys
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Pokémon TCG similar deck search
61
card normalize map
data/uniq_*.txt
new deck
metadata
known deck
sqlite3
sqlite3
My MacBook
App
deck
similarity
Search
Initialize
Crowler
Web UI
@deck
@idf
@norm
@name
@id_norm
Make Vector
Update Deck
Build page
periodic task
検索の高速化
case 2
n decks
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Pokémon TCG similar deck search
62
card normalize map
data/uniq_*.txt
new deck
metadata
known deck
sqlite3
sqlite3
My MacBook
App
deck
similarity
Search
Initialize
Crowler
Web UI
@deck
@idf
@norm
@name
@id_norm
Make Vector
Update Deck
Build page
periodic task
検索の高速化
n decks
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Ractor-ize
Using Ractor is easy, but writing for Ractor is hard.
Refactoring was very hard
Add decks while searching
I want an appendable shared object
追記可能な共有可能なオブジェクトがほしいです！
63
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変化がない領域だけRactorで検索する
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if USING_RACTOR
top = (_search_by_deck_core(@added_deck, v, n) + search_using_ractor(v, n)).max(n)
else
top = _search_by_deck_core(@deck, v, n)
end
search added decks
(w/o Ractor)
search static decks
(Ractor)
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大きな配列を作るのを嫌ってイテレータを作った
65
def each_in(range)
return enum_for(__method__, range) unless block_given?
range.each {|x| yield(@deck[x])}
end
def _search_by_deck(v, n)
(([email protected]).step(@deck.size / @nproc) + [@deck.size]).each_cons(2).map {|s, e|
Ractor.new(self, each_in(s...e), v, n) {|world, sub_decks, v, n|
world._search_by_deck_core(sub_decks, v, n)
}
}.map {|r| r.take}.sum([]).max(n)
end
enum of subrange
subrange iterator
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10Ractors, 46400 decks
66
0...5811
5911...11622
11622...17433
17433...23244
23244...29055
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map { Ractor.new }.map {|r| r.take }.sum.max
max(n)を集めてmax(n)
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.map {|r| r.take}
e.map {Ractor.new(r) {|it| it.max(n)}}
[ ]
[ ]
.sum([]).max(n)
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10Ractors, 46400 decks
だいぶ速くなった！
68
M1 Pro MacBook
w/o Ractor
0.272264 sec
Ractor
0.070405 sec
OCI ARM4 core
w/o Ractor
0.369918735 sec
Ractor
0.099914693 sec
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Peak performance issue
When tweeting, search slows down
Multiple requests generate a lot of Ractor
宣伝ツイートすると検索がめっちゃ遅くなる
69
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Agenda
Concurrency is everywhere
Introduction to Ractor
case study
70
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Pokémon
Trainer Energy
Pokémon TCG
Build a deck with 60 cards