Reactive Streams, j.u.concurrent, & Beyond!

Konrad `ktoso` Malawski @ QCon San Francisco, 2016
pic: 1:1 scale Gundam model @ Odaiba, Tokyo
Reactive Streams, j.u.concurrent, & Beyond!

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streams-jdk

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Past => Present => Future
Agenda:

Underlying motto.
For this talk,
and our continued research.
“We can do better than that.”

Konrad `ktoso` Malawski
Akka Team,
Reactive Streams TCK,
Persistence, Streams & HTTP, Core, Remoting

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

Reactive Streams, j.u.concurrent, & Beyond!

Make building powerful concurrent &
distributed applications simple.
Akka is a toolkit and runtime
for building highly concurrent,
distributed, and resilient
message-driven applications
on the JVM

Actors – simple & high performance concurrency
Cluster / Remoting – location transparency, resilience
Cluster Sharding – and more prepackaged patterns
Streams – back-pressured stream processing
Persistence – Event Sourcing
HTTP – complete, fully async and reactive HTTP Server
Oﬀicial Kafka, Cassandra, DynamoDB integrations, tons
more in the community
Complete Java & Scala APIs for all features (since day 1)
Typed coming soon…

And the many meanings it carries.
Reactive

The many meanings of Reactive
reactivemanifesto.org

Reactive… on the Application level
Reactive Apps

So what are Reactive Streams actually?

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

Single Reactive App
Any benefits?

Reactive on the Application level
https://speakerdeck.com/benjchristensen/applying-rxjava-to-existing-applications-at-philly-ete-2015

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

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

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

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?

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

“slamming the breaks”

We’ll re-visit this speciﬁc case in a bit :-)
“slamming the breaks”

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

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

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

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

“Stream”
What does it mean?!

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.

Suddenly everyone jumped on the word “Stream”.
Akka Streams / Reactive Streams started end-of-2013.
“Streams”
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 / … :-)

The specification.
Reactive Streams
Origins of

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

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

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.
}

Play Iteratees – pull back-pressure, difficult API
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
Akka-IO – NACK back-pressure; low-level IO (Bytes); messaging API
RxJava – no back-pressure, nice API

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.
Afterwards more organisations are invited to join the effort, including Pivotal, RedHat etc.

October 2013
The term “reactive non-blocking asynchronous back-pressure” gets coined.Goals:
- asynchronous
- never block (waste)
- safe (back-threads pressured)
- purely local abstraction
- allow synchronous impls.

October 2013
December 2013
Stephane Maldini & Jon Brisbin (Pivotal Reactor) contacted by Viktor.

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.

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 :-)

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

But what does it do!?
Reactive Streams

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

Fast Publisher Slow Subscriber
Push model

Subscriber usually has some kind of buffer.
Push model

What if the buffer overﬂows?
Push model

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

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

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

Reactive Streams explained
Reactive Streams
explained in 1 slide

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

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 – 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

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!

Argh, implementing a correct RS Publisher
or Subscriber is so hard!

Argh, implementing a correct
RS Publisher or Subscriber is so hard!
You should be using
Akka Streams instead!

Already made a huge industry impact
Reactive Streams

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

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

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!

Source.single(1).map(i -> i.toString).runWith(Sink.head())
// types: _
Source<Int, NotUsed>
Flow<Int, String, NotUsed>
Sink<String, Future<String>>

Source.single(1).map(i -> i.toString).runWith(Sink.head())
// types: _
Source<Int, Unit>
Flow<Int, String, Unit>
Sink<String, Future<String>>

Materialization
Gears from GeeCON.org, did I mention it’s an awesome conf?

What is “materialization” really?

Akka Streams & HTTP
streams
& HTTP

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

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

Streaming in Akka HTTP
DEMO
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

DEMO
HttpServer as a:
HTTP Entity as a:
Source[ByteString, _]

DEMO
HttpServer as a:
HTTP Entity as a:
Source[ByteString, _]
Websocket connection as a:
Flow[ws.Message, ws.Message]

It’s turtles buffers all the way down!

Streaming from Akka HTTP (Java)
public static void main(String[] args) {
final ActorSystem system = ActorSystem.create();
final Materializer materializer = ActorMaterializer.create(system);
final Http http = Http.get(system);
final Source<Tweet, NotUsed> tweets = Source.repeat(new Tweet("Hello world"));
final Route tweetsRoute =
path("tweets", () ->
completeWithSource(tweets, Jackson.marshaller(), EntityStreamingSupport.json())
);
final Flow<HttpRequest, HttpResponse, NotUsed> handler =
tweetsRoute.flow(system, materializer);
http.bindAndHandle(handler,
ConnectHttp.toHost("localhost", 8080),
materializer
);
System.out.println("Running at http://localhost:8080");
}

Streaming from Akka HTTP (Scala)
object Example extends App
with SprayJsonSupport with DefaultJsonProtocol {
import akka.http.scaladsl.server.Directives._
implicit val system = ActorSystem()
implicit val mat = ActorMaterializer()
implicit val jsonRenderingMode = EntityStreamingSupport.json()
implicit val TweetFormat = jsonFormat1(Tweet)
def tweetsStreamRoutes =
path("tweets") {
complete {
Source.repeat(Tweet(""))
}
}
Http().bindAndHandle(tweetsStreamRoutes, "127.0.0.1", 8080)
System.out.println("Running at http://localhost:8080");
}

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

Codename:
Alpakka
// these are “Alpacasso”

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

Threading & Concurrency in Akka Streams Explained (part I)
Mastering GraphStages (part I, Introduction)
Akka Streams Integration, codename Alpakka
A gentle introduction to building Sinks and Sources using GraphStage APIs
(Mastering GraphStages, Part II)
Writing Akka Streams Connectors for existing APIs
Flow control at the boundary of Akka Streams and a data provider
Akka Streams Kafka 0.11

Existing examples:
MQTT
AMQP
Streaming HTTP
Streaming TCP
Streaming FileIO
Cassandra Queries
“Reactive Kafka” (akka-stream-kafka)
S3, SQS & other Amazon APIs
Streaming JSON
Streaming XML

Existing examples:
MQTT
AMQP
Streaming HTTP
Streaming TCP
Streaming FileIO
Cassandra Queries
“Reactive Kafka” (akka-stream-kafka)
S3, SQS & other Amazon APIs
Streaming JSON Parsing
Streaming XML Parsing

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

ReactiveSocket.io
Taking it to the next level:

ReactiveSocket.io
Taking it to the next level:
A collaboration similar in spirit, and continuing from where
Reactive Streams brought us today.

Reactive Streams over network boundaries
http://reactivesocket.io/
Reactive Streams = async boundaries
Reactive Socket = RS + network boundaries

Reactive Streams = async boundaries
Reactive Socket = RS + network boundaries
Primarily led by:
- Ben Christensen, Todd Montgomery (Facebook) & team
- Nitesh Kant (Netﬂix) & team
Lightbend on board as well – right now we’re prototyping with it.

“ReactiveSocket is an application protocol
providing Reactive Streams semantics
over an asynchronous, binary boundary.”

Binary
Support various platforms:
- java
- c++
- js
- …

Async
Obviously we want it to be
async and properly bi-directional.

Application
protocol
Again, bridging app-level
semantics to wire semantics.
Reactive Streams semantics:
- “you can do 10 requests”

Application
protocol
Again, bridging app semantics
to wire semantics.
Reactive Streams semantics:
- “you can do 10 requests”
Extra Lease semantics:
- “you can do 10 reqs in 30secs”

Lease semantics, “flipping the problem”

State and Future[_] of Reactive
Reactive Systems – well established “goal” architecture
…excellent building blocks available, and getting even better with:
Reactive-Streams eco-system blooming!
… as very important building block of the puzzle.
Akka Streams driving implementation of Reactive Streams
(ﬁrst passing TCK, prime contributor to spec, strong ecosystem)
Reactive Socket continuing to improve app-level ﬂow-control
semantics. More control than “just use HTTP/2”.
… considering resumability for streams as well.
The best is yet to come:
combining all these components into resilient, scalable systems!

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

Pics
Gundam pictures from: http://www.wallpaperup.com/tag/gundam/3

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 :-)

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

Watch the video with slide
synchronization on InfoQ.com!
https://www.infoq.com/presentations/
streams-jdk

Reactive Streams, j.u.concurrent, & Beyond!

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