[Japanese] How Reactive Streams and Akka Streams change the JVM Ecosystem @ Reactive Shinjuku

Konrad 'ktoso' Malawski
GeeCON 2014 @ Kraków, PL
Konrad `@ktosopl` Malawski @ Reactive Systems Meetup
streams
How reactive streams
change the JVM Ecosystem
&
reative streams と akka streams が
JVMエコシステムをどのように変えるか

Konrad `ktoso` Malawski
(we’re renaming soon!)
Akka Team,
Reactive Streams TCK,
Maintaining Akka Http
TypesafeのKonrad `ktoso` Malawskiです。
(Typesafeもうすぐ会社名を変更します）

Konrad `@ktosopl` Malawski
akka.io
typesafe.com
geecon.org
Java.pl / KrakowScala.pl
sckrk.com / meetup.com/Paper-Cup @ London
GDGKrakow.pl
lambdakrk.pl
(we’re renaming soon!)

Nice to meet you!
Who are you guys?
はじめまして！

Agenda for today:
• Story & landscape
• The Reactive Streams Protocol
• Akka Streams / Demo
• Akka Http / Demo
• Q/A?
本日のアジェンダです。経緯と展望、Reacive Streams プロ
トコル、Akka Streamsとデモ、Akka Http とデモ、Q/A

Reactive Streams - story: early FRP
http://blogs.msdn.com/b/rxteam/archive/2009/11/17/announcing-reactive-extensions-rx-for-net-silverlight.aspx
http://infoscience.epﬂ.ch/record/176887/ﬁles/DeprecatingObservers2012.pdf - Ingo Maier, Martin Odersky
https://github.com/ReactiveX/RxJava/graphs/contributors
https://github.com/reactor/reactor/graphs/contributors
https://medium.com/@viktorklang/reactive-streams-1-0-0-interview-faaca2c00bec#.69st3rndy
- .NETs’ Reactive Extensions
.NET 3.5
Reactive Streams のストーリー: 初期のFRP
.NET の Reactive Extensions がリリースされました。

Reactive Streams - story: 2013’s impls
~2013:
Reactive Programming
becoming widely adopted on JVM.
- Play introduced “Iteratees”
- Akka (2009) had Akka-IO (TCP etc.)
- Ben starts work on RxJava
Teams discuss need for back-pressure
in simple user API.
Play’s Iteratee / Akka’s NACK in IO.
}
2013年には Reactive Programming が JVM 上で広く採用されるようになりました。
Play,Akkaのチームは簡潔なユーザAPIによる背圧制御の必要性を議論していました。

Play Iteratees – pull back-pressure, difﬁcult API
Akka-IO – NACK back-pressure; low-level IO (Bytes); messaging API
RxJava – no back-pressure, nice API
Play の Iteratee は pull 型の背圧制御でAPIが難しく、Akka-IO は NACK 背圧制御で
低レベルI/OとメッセージングAPI, RxJava は背圧制御はないが優れたAPIでした。

Reactive Streams - Play’s Iteratees
def fold[B](
done: (A, Input[E]) => Promise[B],
cont: (Input[E] => Iteratee[E, A]) => Promise[B],
error: (String, Input[E]) => Promise[B]
): Promise[B]
// an iteratee that consumes chunkes of String and produces an Int
Iteratee[String,Int]
https://www.playframework.com/documentation/2.0/Iteratees
Feb 2013
Iteratees solved the back-pressure problem,
but were hard to use.
Iteratee & Enumeratee – Haskell inspired.
Play / Akka teams looking for common concept.
2013年2月, Play の Iteratee は背圧制御の問題を解決しましたが使いづらく、
Play/Akkaのチームは共通のコンセプトを探していました。

Reactive Streams - expert group founded
October 2013
Roland Kuhn (Akka) and Erik Meijer (Rx .NET) meet in Lausanne,
while recording “Principles of Reactive Programming” Coursera Course.
Viktor Klang (Akka), Erik Meijer, Ben Christensen (RxJava)
and Marius Eriksen (Twitter) meet at Twitter HQ.
The term “reactive non-blocking asynchronous back-pressure” gets coined.
2013年10月, Roland Kuhn(Akka)、Erik Meijer (Rx .NET) らにより"リアクティブ
ノンブロッキング非同期背圧制御"という言葉が造り出されました。

October 2013
Goals:
- asynchronous
- never block (waste)
- safe (back-threads pressured)
- purely local abstraction
- allow synchronous impls.
Also, for our examples today:
- compatible with TCP
非同期、ブロックしない、安全、純粋にローカルのような抽象化、同期の実装も許すこ
とをゴールとしました。また、今日の前例にならいTCPとの互換性も目指しました。

October 2013
December 2013
Stephane Maldini & Jon Brisbin (Pivotal Reactor) contacted by Viktor.
Sephane Maldini と Jon Brisbin (Pival Reactor) がViktor にコン
タクトしました。

October 2013
December 2013
Soon after, the “Reactive Streams” expert group is formed.
Also joining the efforts: Doug Lea (Oracle), EndreVarga (Akka), Johannes Rudolph &  
Mathias Doenitz (Spray), and many others, including myself join the effort soon after.
まもなく "Reactive Streams" エキスパートグループが創設されました。
Doug Lea(Oracle)や、私自身も含めた多くの人々が参画しました。

October 2013
December 2013
Soon after, the “Reactive Streams” expert group is formed.
Also joining the efforts: Doug Lea (Oracle), EndreVarga (Akka), Johannes Rudolph &  
Mathias Doenitz (Spray), and many others, including myself join the effort soon after.
I ended up implementing much of the TCK.
Please use it, let me know if it needs improvements :-)
私はTCK(Technology Compatibility Kit) の大部分の実装を終えました。
ぜひ使ってみて、もし改良が必要なら教えてください :-)

2014–2015:
Reactive Streams Spec & TCK
development, and implementations.
1.0 released on April 28th 2015,
with 5+ accompanying implementations.
2015
Proposed to be included with JDK9 by Doug Lea
via JEP-266 “More Concurrency Updates”
http://hg.openjdk.java.net/jdk9/jdk9/jdk/ﬁle/6e50b992bef4/src/java.base/share/classes/java/util/concurrent/Flow.java
2014-2015に Reactive Streams の仕様とTCKが開発・実装され、
2015年4月28日に 1.0 が5つ以上の実装と共にリリースされました。

2014–2015:
Reactive Streams Spec & TCK
development, and implementations.
1.0 released on April 28th 2015,
with 5+ accompanying implementations.
2015
Proposed to be included with JDK9 by Doug Lea
via JEP-266 “More Concurrency Updates”
http://hg.openjdk.java.net/jdk9/jdk9/jdk/ﬁle/6e50b992bef4/src/java.base/share/classes/java/util/concurrent/Flow.java
Doug Lea により、これを JDK9 に含めることが
JEP-266 "More Concurrency Updates" を通じて提案されました。

in a few words:
• Toolkit for building scalable distributed / concurrent apps.
• High Performance Actor Model implementation
• “share nothing” – messaging instead of sharing state
• millions of msgs, per actor, per second
• Supervision trees – built-in and mandatory
• Clustering and Http built-in
A
B
BarFoo
C
B
E
A
D
C
/Foo
/Foo/A
/Foo/A/B
/Foo/A/D
Guardian System Actor
Name resolution—like a file-system
akka はスケーラブルな分散/並行アプリを構築するためのツールキットです。高性能な
Actorモデルの実装で、Supervisionツリー、クラスタリング/Httpを組込んでいます。

Why back-pressure?
?
なぜ背圧制御なのでしょうか？

Why back-pressure?
So you’ve built your app and it’s awesome.
皆さんはアプリを構築してきました、
それは素晴らしいことです。

Why back-pressure?
Let’s not smash it horribly under load.
負荷をかけすぎて、壊さないようにしましょう。

What is back-pressure?
?
背圧制御(back pressure) とは何でしょうか？

背中(back)が痛くて死にそうだ!

No no no…!
Not THAT Back-pressure!
No no no…!
Not THAT Back-pressure!
いやいやいや...! その back pressure ではありません！

Publisher[T] Subscriber[T]
Back-pressure explained
背圧制御の説明

Fast Publisher Slow Subscriber
What if…?
もしも、1秒間に100の命令を処理する高速なPublisherと、
1秒間に1つの命令しか処理しない低速なSuscriberだったら?

Push + NACK model
Push と NACK モデル

Push + NACK model
Subscriber usually has some kind of buffer.
Subscriber は普通ある種のバッファを持っています。

What if the buffer overﬂows?
Push + NACK model
もしバッファがオーバーフローしたら何が起こるでしょう?

Use bounded buffer,
drop messages + require re-sending
Push + NACK model
大きさが限られたバッファを使い、
メッセージを廃棄し、再送を要求します。

Kernel does this!
Routers do this!
(TCP)
Use bounded buffer,
drop messages + require re-sending
Push + NACK model
カーネルがこれを行います! ルーターがこれを行います! (TCP)

Increase buffer size…
Well, while you have memory available!
Push + NACK model
バッファサイズを増やしてみましょう...
そう、使用できるメモリがある限りは!

Push + NACK model
メモリ不足

NACKing
Buffer overﬂow is imminent!
バッファオーバーフローが今にも起こりそうです!

Telling the Publisher to slow down / stop sending…
NACKing
Publisher にスローダウンするか、送信を止めるよう知らせます...

NACK did not make it in time,
because M was in-ﬂight!
NACKing
NACK は間に合いませんでした、
メッセージが飛んできている最中だったからです!

What if…
We don’t need to back-pressure, because:
speed(publisher) < speed(subscriber)
We need low-overhead for “happy case”
もしも、publisher のスピード < subscrber のスピードであれば、
背圧制御は必要ありません。

No problem!
Fast Subscriber => no problem
高速な Subscriber であれば、問題ありません!

Back-pressure?
Reactive-Streams
=
“Dynamic Push/Pull”
Fast Subscriber => no problem
背圧制御? Reactive Streams = "動的な Push/Pull"

Just push – not safe when Slow Subscriber
Just pull – too slow when Fast Subscriber
Reactive Streams: “dynamic push/pull”
push だけだと、Subscriberが低速な時には、安全ではありません。
pull だけだと、Subscriberが高速な時には、遅すぎます。

Solution:
Dynamic adjustment
Just push – not safe when Slow Subscriber
Just pull – too slow when Fast Subscriber
解決策は、動的な調整です。

Slow Subscriber sees it’s buffer can take 3 elements.
Publisher will never blow up its buffer.
低速なSubscriberはバッファに3要素入れられることがわかります。
Publsherはバッファをあふれさせることは決してありません。

Fast Publisher will send at-most 3 elements.
This is pull-based-backpressure.
高速なPublisherは、高々3つの要素を送ります。
これがpullベースの背圧制御です。

Fast Subscriber can issue more Request(n),
before more data arrives!
高速なSubscriberは、データが到着する前に、
より多くのリクエストを発行することができます!

Fast Subscriber can issue more Request(n),
before more data arrives.
Publisher can accumulate demand.
高速なSubscriberは、需要を蓄積することができます。

Publisher accumulates total demand per subscriber.
Reactive Streams: accumulate demand
Publisherはsubscriberごとに需要の合計を蓄積します。

Total demand of elements is safe to publish.
Subscriber’s buffer will not overﬂow.
Reactive Streams: accumulate demand
需要の合計分の要素をpublishしても安全です。
Subscriberのバッファはオーバーフローしないでしょう。

http://reactive-streams.org
We want to make different implementations
co-operate with each other.
Reactive Streams: Inter Op
私たちは、異なる実装がお互いに協力できるようにしたいのです。

RS is NOT a “daily use”, “end-user” API.
It’s an SPI - Service Provider Interface.
Reactive Streams: Inter-Op
https://en.wikipedia.org/wiki/Service_provider_interface
Service Provider Interface (SPI) is an API intended to be
implemented or extended by a third party.
RS は "毎日使用する", "エンドユーザ向けの" API ではなく、サードパーティにより
実装される、または拡張される SPI (Service Provider Interface) です。

EmbeddedApp.fromHandler(new Handler {
override def handle(ctx: Context): Unit = {
// RxJava Observable
val intObs = Observable.from((1 to 10).asJava)
// Reactive Streams Publisher
val intPub = RxReactiveStreams.toPublisher(intObs)
// Akka Streams Source
val stringSource = Source(intPub).map(_.toString)
// Reactive Streams Publisher
val stringPub = stringSource.runWith(Sink.fanoutPublisher(1, 1))
// Reactor Stream
val linesStream = Streams.create(stringPub).map[String](
new reactor.function.Function[String, String] {
override def apply(in: String) = in + "n"
})
// and now render the HTTP response (RatPack)
ctx.render(ResponseChunks.stringChunks(linesStream))
}
}).test(new Consumer[TestHttpClient] {
Reactive Streams: Inter-Op
https://en.wikipedia.org/wiki/Service_provider_interface

Akka Streams & HTTP
streams
& HTTP

Akka Streams in 20 seconds:
// types:
Source[Out, Mat]
Flow[In, Out, Mat]
Sink[In, Mat]
// generally speaking, it's always:
val ready = Source(???).via(flow).map(_ * 2).to(sink)
val mat: Mat = ready.run()
// the usual example:
val f: Future[String] =
Source.single(1).map(_.toString).runWith(Sink.head)
Proper static typing!
Akka Streams を 20秒で説明します。
Source, Flow, Sink に適切な静的型付けをします。

// types: _
Source[Int, akka.NotUsed]
Flow[Int, String, akka.NotUsed]
Sink[String, Future[String]]

// types: _
Source[Int, Unit]
Flow[Int, String, Unit]
Sink[String, Future[String]]

Akka HTTP
Joint effort of Spray and Akka teams.
Complete HTTP Server/Client implementation.
Soon prod ready, developed ~1.5 years.
Learns from Spray’s 3-4 years history.
Since the beginning with
streaming as ﬁrst class citizen.
Akka HTTP は、SprayとAkkaチームが協力して開発した、完全なHTTPサーバ/クライアント
実装です。1年半かけて開発されており、間もなく業務に耐えうるレベルになるでしょう。

It’s turtles buffers all the way down!
親亀の上に子亀、子亀の上に孫亀がのるかのように、バッファの
次にバッファ、その次にまたバッファ... と無数につらなります!

Streaming from Akka HTTP
Akka HTTP からのストリーミング

No demand from TCP
=
No demand upstream
=
Source won’t generate tweets
TCPからの要求がない = 上流の要求がない
= Source はツイートを生成しません

No demand from TCP
=
No demand upstream
=
=>
TCPからの要求がない = 上流の要求がない
= Source はツイートを生成しません

No demand from TCP
=
No demand upstream
=
=>
Bounded memory
stream processing!
=>大きさが制限されたメモリーでのストリーム処理!

Client / Server “JSON Streaming” demo
Demo time
クライアント/サーバの "JSONストリーミング" デモ

Akka Streams
Hidden powers:
Parallelism
&&
Pipelining
Akka Streams の隠されたパワー:
並列処理(Parallelism)とパイプライン

Pipelining Pancakes
http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0/scala/stream-parallelism.html

Pipelining
// Takes a scoop of batter and creates a pancake with one side cooked
val fryingPan1: Flow[ScoopOfBatter, HalfCookedPancake, Unit] =
Flow[ScoopOfBatter].map { batter => HalfCookedPancake() }
// Finishes a half-cooked pancake
val fryingPan2: Flow[HalfCookedPancake, Pancake, Unit] =
Flow[HalfCookedPancake].map { halfCooked => Pancake() }
// With the two frying pans we can fully cook pancakes
val pancakeChef: Flow[ScoopOfBatter, Pancake, Unit] =
Flow[ScoopOfBatter].via(fryingPan1).via(fryingPan2)

Parallelism
val fryingPan: Flow[ScoopOfBatter, Pancake, Unit] =
Flow[ScoopOfBatter].map { batter => Pancake() }
val pancakeChef: Flow[ScoopOfBatter, Pancake, Unit] = Flow() {
implicit builder =>
val dispatchBatter = builder.add(Balance[ScoopOfBatter](2))
val mergePancakes = builder.add(Merge[Pancake](2))
dispatchBatter.out(0) ~> fryingPan ~> mergePancakes.in(0)
dispatchBatter.out(1) ~> fryingPan ~> mergePancakes.in(1)
(dispatchBatter.in, mergePancakes.out)
}

Parallelism
val fryingPanFun: ScoopOfBatter ⇒ Future[Pancake] =
batter ⇒ Future.successful(Pancake())
val pancakeChef: Flow[ScoopOfBatter, Pancake, Unit] =
Flow[ScoopOfBatter].mapAsync(parallelism = 2)(fryingPanFun)
Or simply “mapAsync”:

Pipelining && Parallelism
Parallelism
&&
Pipelining
do the heavy-work for you.
並列処理とパイプラインは、あなたのために
きつい作業をやってくれます。

10/26/2015 spray-can: add websockets support (client & server) · Issue #134 · spray/spray
Pull requests Issues GistThis repository Search
2,092 496197Watch Star Forkspray / spray
and others
Labels
Milestone

akka-http
Assignee
No one assigned
111 participants
spray-can: add websockets support (client & server) #134
Closed sirthias opened this issue on Sep 4, 2012 · 129 comments
New issue
Feature
Notifications
You’re not receiving
notifications from this
thread.
Subscribe
Ownersirthias commented on Sep 4, 2012
No description provided.
analytically commented on Oct 23, 2012
+1
tommcp commented on Nov 1, 2012
+1
t3hnar commented on Nov 10, 2012
+1
alexbool commented on Nov 10, 2012
+1
olger commented on Nov 16, 2012
+1
pjean commented on Nov 29, 2012
+1
edgurgel commented on Nov 29, 2012
+1
zerni commented on Dec 10, 2012
+1
Bathtor commented on Dec 10, 2012
+1
WebSockets
A.K.A.
“Spray’s single most upvoted
feature request ever”
98 * “+1”
WebSockets は、"Spray のこれまでの機能リクエストのなかで、
単独で最も票を集めた機能" としても知られています。

Spray’s most requested feature ever:
WebSockets
path("ws") {
val handler: Flow[Message, Message] = ???
handleWebsocketMessages(handler)
}

Spray’s most requested feature ever:
WebSockets
path("ws") {
val handler = Flow.fromSinkAndSource(
Sink.ignore,
Source.single(TextMessage("Hello World!”)))
handleWebsocketMessages(handler)
}

Summing up…
Summing up…
要約...

buffers, buffers everywhere!
https://dev.twitter.com/streaming/overview/request-parameters#stallwarnings
あらゆるところにバッファが!

JEP-266 – soon…!
public final class Flow {
private Flow() {} // uninstantiable
@FunctionalInterface
public static interface Publisher<T> {
public void subscribe(Subscriber<? super T> subscriber);
}
public static interface Subscriber<T> {
public void onSubscribe(Subscription subscription);
public void onNext(T item);
public void onError(Throwable throwable);
public void onComplete();
}
public static interface Subscription {
public void request(long n);
public void cancel();
}
public static interface Processor<T,R> extends Subscriber<T>, Publisher<R> {
}
}
JEP-266 がまもなく...!

Back-pressure as a feature
特徴(目玉機能)としての背圧制御

Roadmap Update: Streams & HTTP
Already pretty mature and complete implementation.
WebSockets!
Play 2.5 (2.5+) uses Akka Streams.
(Scala || Java) DSL == same power.
Last phases of polishing up APIs and features.
2.4.2 release in coming weeks.
Akka 2.4 requires JDK8.
(that’s about time to do so!)
StreamsとHTTPの最新ロードマップ
既に十分成熟しており、実装が完了しました。

• Reactive Platform
• Remoting / Cluster: Docker networking support
• Cluster: Split Brain Resolver (beta)
• Akka Persistence: Cross-Scala-version snapshot deserializer
• Java 6: Extended LTS
• Akka 2.4.2 (released this month, binary compatible with 2.3)
• Akka Streams becomes non experimental in 2.4.2!
• Cluster Tools promoted to stable!
• Persistence promoted to stable!
• Persistence Queries (experimental)
• Akka Typed (experimental)
• Distributed Data (experimental)
Roadmap Update: Akka
Akka の最新ロードマップ

Links
• The projects:
• akka.io
• typesafe.com/products/typesafe-reactive-platform
• reactive-streams.org 
• Viktor Klang’s interview with all RS founding members
• Akka HTTP in depth with Mathias and Johannes @ Scala.World
• Akka User - mailing list:
• https://groups.google.com/group/akka-user
• Community chat:
• http://gitter.im/akka/akka

Thanks!
onNext(Q/A)
(Now’s the time to ask things!)
ktoso @ typesafe.com
twitter: ktosopl
github: ktoso
team blog: letitcrash.com
home: akka.io

[Japanese] How Reactive Streams and Akka Streams change the JVM Ecosystem @ Reactive Shinjuku

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (18)

Similar to [Japanese] How Reactive Streams and Akka Streams change the JVM Ecosystem @ Reactive Shinjuku

Similar to [Japanese] How Reactive Streams and Akka Streams change the JVM Ecosystem @ Reactive Shinjuku (20)

Recently uploaded

Recently uploaded (20)

[Japanese] How Reactive Streams and Akka Streams change the JVM Ecosystem @ Reactive Shinjuku