1.
Konrad `ktoso` Malawski @ Scala Matsuri 2017 @ Tokyo
Survival guide for the
Streaming World
Akka-chan’s

2.
Me, Tokyo & Akka Streams…
The road to 2017…
2014年: リアクティブストリーム始動

3.
0.7
Early preview
RS started (late 2013)
2014
2016年: JDK 9 への導入決定
Reactive Streams started in late 2013,
initiated by Roland Kuhn,Viktor Klang, Erik Meijer, Ben Christiansen

4.
0.7
Early preview
RS begins!
2.0
Experimental
RS on way to JDK9
2014 2016
2014年: リアクティブストリーム始動2017年: 安定版。リアクティブストリーム付きの JDK9。

5.
0.7
Early preview
RS begins!
2.0
Experimental
RS on way to JDK9
2.4.17 + 10.0.3
Fully stable
RS in JDK9 (!)
2014 2016 2017
2014年: リアクティブストリーム始動2016年: JDK 9 への導入決定

6.
Konrad `ktoso` Malawski
Akka Team,
Reactive Streams TCK,
Scala SLIP Committee member

7.
Konrad `@ktosopl` Malawski
work: akka.io lightbend.com
personal blog: http://kto.so
communities: geecon.org Java.pl / KrakowScala.pl sckrk.com GDGKrakow.pl lambdakrk.pl

8.
Konrad `@ktosopl` Malawski
work: akka.io lightbend.com
personal blog: http://kto.so
communities: geecon.org Java.pl / KrakowScala.pl sckrk.com GDGKrakow.pl lambdakrk.pl

9.
2017年: 安定版。リアクティブストリーム付きの JDK9。

10.
The concurrent & distributed applications toolkit
Akka is a toolkit and runtime for building
highly concurrent, distributed, and resilient
message-driven applications on the JVM
2017年: 安定版。リアクティブストリーム付きの JDK9。

11.
Actors – Concurrency / high perf. messaging
Cluster – Location transparent, resilient clusters
Persistence – EventSourcing support (many DBs)
Distributed Data – Availability-first gossiped CRDTs
HTTP – Fully Async & Reactive Http Server (Websockets, Http/2)
soon:
Typed – really well-typed Actors, coming this year
…and much more (kafka, cassandra, testing, …)

12.
“Stream”

13.
“Stream”
なんですか？
2017年: 安定版。リアクティブストリーム付きの JDK9。

14.
Suddenly everyone jumped on the word “Stream”.
Akka Streams / Reactive Streams started end-of-2013.
“Streams”
* when put in “” the word does not appear in project name, but is present in examples / style of APIs / wording.

15.
Suddenly everyone jumped on the word “Stream”.
Akka Streams / Reactive Streams started end-of-2013.
The word “Stream” is used in many contexts/meanings
Akka Streams
Reactive Streams
RxJava “streams”*
Spark Streaming
Apache Storm “streams”*
Java Steams (JDK8)
Reactor “streams”*
Kafka Streams
ztellman / Manifold (Clojure)
* when put in “” the word does not appear in project name, but is present in examples / style of APIs / wording.
Apache GearPump “streams”
Apache [I] Streams (!)
Apache [I] Beam “streams”
Apache [I] Quarks “streams”
Apache [I] Airflow “streams” (dead?)
Apache [I] Samza
Scala Stream
Scalaz Streams, now known as FS2
Swave.io
Java InputStream / OutputStream / … :-)
2017年: 安定版。リアクティブストリーム付きの JDK9。

16.
“Stream”
What does it mean?!
• Possibly infinite datasets (“streams”)
• “Streams are NOT collections.”
• Processed element-by-element
• Element could mean “byte”
• More usefully though it means a specific type “T”
• Asynchronous processing
• Asynchronous boundaries (between threads)
• Network boundaries (between machines)
2017年: 安定版。リアクティブストリーム付きの JDK9。

17.
The new “big data” is “fast data”.
Enabled by reactive building blocks.
“Fast Data”
2017年: 安定版。リアクティブストリーム付きの JDK9。

18.
The typical “big data” architecture
ever since Hadoop entered the market
Legacy Hadoop architectures would mostly look like this:
(legacy, batch oriented architectures)
典型的な「ビッグデータ」アーキテクチャ

19.
The typical “big data” architecture
ever since Hadoop entered the market
Legacy Hadoop architectures would mostly look like this:
(legacy, batch oriented architectures)
典型的な「ビッグデータ」アーキテクチャ

20.
Fast Data – architecture example
Many of these technologies compose together into what we call Fast Data architectures.
典型的な「ビッグデータ」アーキテクチャ

21.
Fast Data – architecture example
It’s like big-data, but FAST - which often implies various kinds of streaming.
典型的な「ビッグデータ」アーキテクチャ

22.
Where does Akka Stream fit?
Akka Streams specifically fits,
if you answer yes to any of these:
• Should it take on public traffic?
• Processing in hot path for requests?
• Integrate various technologies?
• Protect services from over-load?
• Introspection, debugging, excellent Akka integration?
• (vs. other reactive-stream impls.)
以上の質問に「はい」と答えた場合は Akka Stream に向いている

23.
How do I pick which “streaming” I need?
Kafka serves best as a transport
for pub-sub across services.
• Note that Kafka Streams (db ops are on the node)
is rather, different than the Reactive Kafka client
• Great for cross-service communication
instead of HTTP Request / Reply
Kafka はサービス間の pub-sub 通信に向いている
HTTP の代わりにサービス間の通信に使う

24.
How do I pick which “streaming” I need?
Spark has vast libraries for ML or join etc ops.
• It’s the “hadoop replacement”.
• Spark Streaming is windowed-batches
• Latency anywhere up from 0.5~1second
• Great for cross-service communication
instead of HTTP Req/Reply
Spark は機械学習系が充実している

25.
Oh yeah, there’s JDK8 “Stream” too!
Terrible naming decision IMHO, since Java’s .stream()
• Geared for collections
• Best for finite and known-up-front data
• Lazy, sync/async (async rarely used)
• Very (!) hard to extend
It’s the opposite what we talk about in Streaming systems!
It’s more:“bulk collection operations”
Also known as… Scala collections API (i.e. Iterator
JDK8 の Stream はイテレータ的なもの

26.
What about JDK9 “Flow”?
JDK9 introduces java.util.concurrent.Flow
• Is a 1:1 copy of the Reactive Streams interfaces
• On purpose, for people to be able to impl. it
• Does not provide useful implementations
• Is only the inter-op interfaces
• Libraries like Akka Streams implement RS,
and expose useful APIs for you to use.
JDK9 の Flow はリアクティブ・ストリーム

27.
And the many meanings it carries.
Reactive

28.
And the many meanings it carries.
Reactive

29.
The many meanings of Reactive
reactivemanifesto.org

30.
The many meanings of Reactive

31.
So what are Reactive Streams actually?

32.
So what are Reactive Streams actually?

33.
So what are Reactive Streams actually?

34.
Getting the complete picture

35.
Getting the complete picture
(yet… not the topic of today’s talk)

36.
How to think about these techniques?
bit.ly/why-reactive

37.
“Not-quite-Reactive-System”
The reason we started researching
into transparent to users flow control.

38.
“Best practices are solutions
to yesterdays problems.”
https://twitter.com/FrankBuytendijk/status/795555578592555008
Circuit breaking as substitute of flow-control

39.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak

40.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak

41.
HTTP/1.1 503 Service Unavailable
HTTP/1.1 503 Service Unavailable
Throttling as represented by 503 responses. Client will back-off… but how?
What if most of the fleet is throttling?

42.
http://doc.akka.io/docs/akka/2.4/common/circuitbreaker.html
HTTP/1.1 503 Service Unavailable
HTTP/1.1 503 Service Unavailable

43.
http://doc.akka.io/docs/akka/2.4/common/circuitbreaker.html

44.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
“slamming the breaks”

45.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
“slamming the breaks”

46.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
“slamming the breaks”

47.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
“slamming the breaks”

48.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
“slamming the breaks”

49.
See also, Nitesh Kant, Netflix @ Reactive Summit
https://www.youtube.com/watch?v=5FE6xnH5Lak
We’ll re-visit this specific case in a bit :-)
“slamming the breaks”

50.
Are absolutely useful!
Still… “We can do better than that.”
Circuit Breakers

51.
But we’ll need everyone to understand
some shared semantics…
We can do better.

52.
A fundamental building block.
Not end-user API by itself.
reactive-streams.org
Reactive Streams

53.
Reactive Streams
More of an SPI (Service Provider Interface),
than API.
reactive-streams.org

54.
The specification.
Reactive Streams
Origins of

55.
What is back-pressure?
?

56.
What is back-pressure?

57.
No no no…!
Not THAT Back-pressure!
Also known as:
flow control.
What is back-pressure?

58.
No no no…!
Not THAT Back-pressure!
Also known as:
application level flow control.
What is back-pressure?

59.
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
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
Teams discuss need for back-pressure
in simple user API.
Play’s Iteratee / Akka’s NACK in IO.
}

60.
Reactive Streams - story: 2013’s impls
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

61.
But what does it do!?
Reactive Streams

62.
Publisher[T] Subscriber[T]
Back-pressure explained

63.
Fast Publisher Slow Subscriber
Push model

64.
Subscriber usually has some kind of buffer.
Push model

65.
Push model

66.
Push model

67.
What if the buffer overflows?
Push model

68.
Use bounded buffer,
drop messages + require re-sending
Push model

69.
Kernel does this!
Routers do this!
(TCP)
Use bounded buffer,
drop messages + require re-sending
Push model

70.
Increase buffer size…
Well, while you have memory available!
Push model

71.
Push model

72.
Reactive Streams explained
Reactive Streams
explained in 1 slide

73.
Fast Publisher will send at-most 3 elements.
This is pull-based-backpressure.
Reactive Streams: “dynamic push/pull”

74.
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> {
}
}

75.
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> {
}
}
Single basic (helper) implementation available in JDK:
SubmissionPublisher

76.
Reactive Streams: goals
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries

77.
Reactive Streams: goals
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries
Argh, implementing a correct
RS Publisher or Subscriber is so hard!

78.
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries
Reactive Streams: goals
Argh, implementing a correct RS Publisher
or Subscriber is so hard!

79.
Reactive Streams: goals
Argh, implementing a correct
RS Publisher or Subscriber is so hard!
You should be using
Akka Streams instead!
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries

80.
Already made a huge industry impact
Reactive Streams

81.
Back-pressure as a feature

82.
Inspiring other technologies

83.
Inspiring other technologies
It’s been a while since Java inspired
other modern technologies, hasn’t it?

84.
The implementation.
Complete and awesome Java and Scala APIs.
As everything since day 1 in Akka.
Akka Streams

85.
Akka Streams in 20 seconds:
// types:
Source[Out, Mat]
Flow[In, Out, Mat]
Sink[In, Mat]
// generally speaking, it's always:
val ready =
Source.from…(???).via(flow).map(i => i * 2).to(sink)
val mat: Mat = ready.run()
// the usual example:
val f: Future[String] =
Source.single(1).map(i => i.toString).runWith(Sink.head)
Proper static typing!

86.
Akka Streams in 20 seconds:
// types: _
Source[Int, NotUsed]
Flow[Int, String, NotUsed]
Sink[String, Future[String]]
Source.single(1).map(i => i / 0).runWith(Sink.head)

87.
Source.single(1).map(i => i.toString).runWith(Sink.head)
// types: _
Source[Int, NotUsed]
Flow[Int, String, NotUsed]
Sink[String, Future[String]]
Akka Streams in 20 seconds:

88.
Your app has specific needs

89.
Your app has specific needs

90.
Your app has specific needs

91.
Your app has specific needs

92.
Your app has specific needs

93.
Ecosystem that solves problems
> (is greater than)
solving all the problems ourselves

94.
Codename:
Alpakka
// these are “Alpacasso”

95.
A community for Streams connectors
Alpakka – a community for Stream connectors
Alp

96.
Alpakka – a community for Stream connectors
http://developer.lightbend.com/docs/alpakka/current/

97.
Alpakka – a community for Stream connectors
http://developer.lightbend.com/docs/alpakka/current/
Krzysztof Ciesielski
Reactive Kafka (Alpakka) maintainer
Room B @ 15:30,1日

98.
Alpakka – a community for Stream connectors
http://developer.lightbend.com/docs/alpakka/current/

99.
Alpakka – a community for Stream connectors
http://developer.lightbend.com/docs/alpakka/current/

100.
Backpressure in action, visualized
streams
& HTTP

101.
A core feature not obvious to the untrained eye…!
Quiz time!
TCP is a ______ protocol?
Akka Streams & HTTP

102.
A core feature not obvious to the untrained eye…!
Quiz time!
TCP is a STREAMING protocol!
Akka Streams & HTTP

103.
Streaming in Akka HTTP
HttpServer as a:
Flow[HttpRequest, HttpResponse]
http://doc.akka.io/docs/akka/2.4/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778

104.
Streaming in Akka HTTP
HttpServer as a:
Flow[HttpRequest, HttpResponse]
HTTP Entity as a:
Source[ByteString, _]
http://doc.akka.io/docs/akka/2.4/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778

105.
Streaming in Akka HTTP
http://doc.akka.io/docs/akka/2.4/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778
HttpServer as a:
Flow[HttpRequest, HttpResponse]
TCP as a:
Flow[ByteString, ByteString]
Websocket connection as a:
Flow[ws.Message, ws.Message]

106.
Streaming Deep Learning Predictions
Akka [Streams/HTTP/Alpakka] + h2o’s Sparkling Water

107.
Streaming Deep Learning Predictions
Akka [Streams/HTTP/Alpakka] + h2o’s Sparkling Water

108.
Streaming Deep Learning Predictions
Akka [Streams/HTTP/Alpakka] + h2o’s Sparkling Water
DEMO TIME

109.
Streaming from Akka HTTP (Java)
wrap(mySource): Source === apply(Source): Source
def bytesToMeasurements(bytes: Source[ByteString, Any]): Source[Double, NotUsed] = {
val measurementLines =
bytes
.via(Framing.delimiter(ByteString("n"), maximumFrameLength = 1000))
.map(_.utf8String)
measurementLines
.via(CsvSupport.takeColumns(Set("LinAccX (g)", "LinAccY (g)", "LinAccZ (g)")))
.mapConcat(_.flatMap(col => Try(col.toDouble).toOption))
.mapMaterializedValue(_ => NotUsed)
}

110.
Streaming from Akka HTTP (Java)
wrap(mySource): Source === apply(Source): Source
def bytesToMeasurements(bytes: Source[ByteString, Any]): Source[Double, NotUsed] = {
val measurementLines =
bytes
.via(Framing.delimiter(ByteString("n"), maximumFrameLength = 1000))
.map(_.utf8String)
measurementLines
.via(CsvSupport.takeColumns(Set("LinAccX (g)", "LinAccY (g)", "LinAccZ (g)")))
.mapConcat(_.flatMap(col => Try(col.toDouble).toOption))
.mapMaterializedValue(_ => NotUsed)
}
via(Flow) === Source.via(Flow)
Flow.via(Flow)
val bytesToMeasurements: Flow[ByteString, Double, NotUsed] = {
val measurementLines =
Flow[ByteString]
.via(Framing.delimiter(ByteString("n"), maximumFrameLength = 1000))
.map(_.utf8String)
measurementLines
.via(CsvSupport.takeColumns(Set("LinAccX (g)", "LinAccY (g)", "LinAccZ (g)")))
.mapConcat(_.flatMap(col => Try(col.toDouble).toOption))
.mapMaterializedValue(_ => NotUsed)
}

111.
Streaming from Akka HTTP (Java)
wrap(mySource): Source === apply(Source): Source
def bytesToMeasurements(bytes: Source[ByteString, Any]): Source[Double, NotUsed] = {
val measurementLines =
bytes
.via(Framing.delimiter(ByteString("n"), maximumFrameLength = 1000))
.map(_.utf8String)
measurementLines
.via(CsvSupport.takeColumns(Set("LinAccX (g)", "LinAccY (g)", "LinAccZ (g)")))
.mapConcat(_.flatMap(col => Try(col.toDouble).toOption))
.mapMaterializedValue(_ => NotUsed)
}
via(Flow) === Source.via(Flow)
Flow.via(Flow)
val bytesToMeasurements: Flow[ByteString, Double, NotUsed] = {
val measurementLines =
Flow[ByteString]
.via(Framing.delimiter(ByteString("n"), maximumFrameLength = 1000))
.map(_.utf8String)
measurementLines
.via(CsvSupport.takeColumns(Set("LinAccX (g)", "LinAccY (g)", "LinAccZ (g)")))
.mapConcat(_.flatMap(col => Try(col.toDouble).toOption))
.mapMaterializedValue(_ => NotUsed)
}

112.
Streaming Deep Learning Predictions
We have demonstrated:
• Superior pluggability and reusability
• Fully type-safe APIs
• Simple custom Stages
• Advanced custom GraphStages

113.
Back-pressure in real systems

114.
Understanding
Reactive Systems

115.
Understanding Systems
At Lightbend we build for
usability, understanding and performance.

116.
Understanding Systems
At Lightbend we build for
usability, understanding and performance.
Remember the car?
That’s only:
“highest possible performance”.

117.
Understanding Systems
We need controllable, introspectable, operable systems.
Monitoring, tracing.
Right at the core of the tech.

118.
Visualising Architectures

119.
Visualising Architectures
https://app.prod.opsclarity.com

120.
Reactive Streams / Akka Streams
Is now the time to adopt?

121.
Totally, go for it.

122.
Happy hAkking!

123.
We <3 contributions
• Easy to contribute:
• https://github.com/akka/akka/issues?q=is%3Aissue+is%3Aopen+label%3Aeasy-to-contribute
• https://github.com/akka/akka/issues?q=is%3Aissue+is%3Aopen+label%3A%22nice-to-have+%28low-prio%29%22
• Akka: akka.io && github.com/akka
• Reactive Streams: reactive-streams.org
• Reactive Socket: reactivesocket.io
• Mailing list:
• https://groups.google.com/group/akka-user
• Public chat rooms:
• http://gitter.im/akka/dev developing Akka
• http://gitter.im/akka/akka using Akka

124.
What’s next for Akka?
• Akka 2.5.x
• Distributed Data becomes stable in 2.5
• Persistence Query becomes stable in 2.5
• Alpakka connectors
• Akka HTTP/2
• Akka HTTP default backend for Play (2.6.0-M1 sic!)
• Akka Typed (sic!)
• Better AbstractActor for Java8 users
• Possible multi-data centre extensions …?
• …?

125.
Find the Akka Ecosystem <3 Scaladex
https://index.scala-lang.org

126.
Free e-book and printed report.
bit.ly/why-reactive
Covers what reactive actually is.
Implementing in existing architectures.
Thoughts from the team that’s building
reactive apps since more than 6 years.
Obligatory “read my book!” slide :-)

127.
ktoso @ lightbend.com
twitter: ktosopl
github: ktoso
team blog: blog.akka.io
home: akka.io
myself: kto.so
Q/A