1.
WEBINAR
Understanding Akka Streams, Back Pressure
and Asynchronous Architectures
by Konrad Malawski (@ktosopl)

2.
Konrad `ktoso` Malawski
Akka Team,
Reactive Streams TCK,
Persistence, HTTP

3.
Konrad `@ktosopl` Malawski
akka.io
typesafe.com
geecon.org
Java.pl / KrakowScala.pl
sckrk.com
GDGKrakow.pl
lambdakrk.pl

4.
“Stream”

5.
“Stream”
What does it mean?!

6.
Akka Streams
Akka Streams && Reactive Streams

7.
Why back-pressure?
?

8.
Why back-pressure?
So you’ve built your app and it’s awesome.

9.
Why back-pressure?
Let’s not smash it horribly under load.

10.
What is back-pressure?
?

11.
What is back-pressure?

12.
No no no…!
Not THAT Back-pressure!
No no no…!
Not THAT Back-pressure!
What is back-pressure?

13.
Publisher[T] Subscriber[T]
Back-pressure explained

14.
Fast Publisher Slow Subscriber
Push model

15.
Subscriber usually has some kind of buffer.
Push model

16.
Push model

17.
Push model

18.
What if the buffer overflows?
Push model

19.
Use bounded buffer,
drop messages + require re-sending
Push model

20.
Kernel does this!
Routers do this!
(TCP)
Use bounded buffer,
drop messages + require re-sending
Push model

21.
Increase buffer size…
Well, while you have memory available!
Push model

22.
Push model
D
EM
O

23.
Reactive Streams explained
Reactive Streams
explained in 1 slide

24.
Fast Publisher will send at-most 3 elements.
This is pull-based-backpressure.
Reactive Streams: “dynamic push/pull”

25.
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> {
}
}

26.
Reactive Streams: goals
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries

27.
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!

28.
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!

29.
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!
You should be using
Akka Streams abstractions instead!

30.
Akka Streams
Streams complement Actors,
they do not replace them.
Actors – distribution (location transparency)
Streams – back-pressured + more rigid-blueprint

31.
Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
Asynchronous processing toolbox:
Power
Constraints

32.
Akka is a Toolkit, pick the right tools for the job.
Asynchronous processing toolbox:
Constraints
Power

33.
Akka is a Toolkit, pick the right tools for the job.
Single value, no streaming by definition.
Local abstraction.
Execution contexts.
Power
Constraints

34.
Akka is a Toolkit, pick the right tools for the job.
Mostly static processing layouts.
Well typed and Back-pressured!
Constraints
Power

35.
Akka is a Toolkit, pick the right tools for the job.
Plain Actor’s younger brother, experimental.
Location transparent, well typed.
Technically unconstrained in actions performed
Constraints
Power

36.
Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
Location transparent.
Various resilience mechanisms.
(watching, persistent recovering, migration, pools)
Untyped and unconstrained in actions performed.
Constraints
Power

37.
Akka Streams
streams

38.
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!

39.
Akka Streams in 20 seconds:
// types: _
Source[Int, Unit]
Flow[Int, String, Unit]
Sink[String, Future[String]]
Source.single(1).map(_.toString).runWith(Sink.head)

40.
Akka Streams in 20 seconds:
// types: _
Source[Int, Unit]
Flow[Int, String, Unit]
Sink[String, Future[String]]
Source.single(1).map(_.toString).runWith(Sink.head)

41.
Materialization
Gears from GeeCON.org, did I mention it’s an awesome conf?

42.
What is “materialization” really?

43.
What is “materialization” really?

44.
What is “materialization” really?

45.
What is “materialization” really?

46.
Akka Streams & HTTP
streams
& HTTP

47.
Akka HTTP
Joint effort of Spray and Akka teams.
Complete HTTP Server/Client implementation.
Learns from Spray’s 3-4 years history.
Since the beginning with
streaming as first class citizen.
Side note:
Lagom also utilises Akka Streams for streaming.

48.
Streaming in Akka HTTP
DEMO
http://doc.akka.io/docs/akka/2.4.7/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778
HttpServer as a:
Flow[HttpRequest, HttpResponse]

49.
Streaming in Akka HTTP
DEMO
http://doc.akka.io/docs/akka/2.4.7/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778
HttpServer as a:
Flow[HttpRequest, HttpResponse]
HTTP Entity as a:
Source[ByteString, _]

50.
Streaming in Akka HTTP
DEMO
http://doc.akka.io/docs/akka/2.4.7/scala/stream/stream-customize.html#graphstage-scala
“Framed entity streaming” https://github.com/akka/akka/pull/20778
HttpServer as a:
Flow[HttpRequest, HttpResponse]
HTTP Entity as a:
Source[ByteString, _]
Websocket connection as a:
Flow[ws.Message, ws.Message]

51.
Persistence Query (experimental)
“The Query Side”
of Akka Persistence

52.
Persistence Query (experimental)
Persistence Query Journals
akka/akka-persistence-cassandra 0.16
akka/akka @ leveldb-journal 2.4.8
dnvriend/akka-persistence-jdbc 2.3.3
scullxbones/akka-persistence-mongo 1.2.5
…and more, that I likely forgot about.
Implementation of “data-pump” pattern.

53.
Akka + Kafka = BFF
Reactive Kafka
+
Started by Krzysiek Ciesielski & Adam Warski @ SofwareMill.com

54.
“ACKnowladged streams”
happy ACKing!

55.
Kafka + Akka = BFF
Akka is Arbitrary processing.
Kafka is somewhat more than a message queue,
but very focused on “the log”.
Spark shines with it’s data-science focus.

56.
Kafka + Akka = BFF

57.
Kafka + Akka = BFF

58.
Streams talking to Actors
&&
Actors talking to Streams

59.
Streams <=> Actors inter-op
Source.actorRef (no back-pressure)
Source.queue (safe)
Sink.actorRef (no back-pressure)
Sink.actorRefWithAck (safe)

60.
Exciting times ahead!

61.
Next steps for Akka
Completely new Akka Remoting (goal: 1M+ msg/s (!)),
(it is built using Akka Streams).
More integrations for Akka Streams stages,
also dynamic fan-in/out A.K.A.“the Hub”.
Reactive Kafka polishing and stable release with SoftwareMill.
“Confirmed Streams” work from Reactive Kafka generalised.
Akka Typed likely to progress again.
Of course, continued maintenance of Cluster and others.

62.
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63.
lightbend.com/pov
Reactive Roundtable
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lightbend.com/reactive-roundtable
Proof of Value Service
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64.
Q/A
ktoso @ lightbend.com
twitter: ktosopl
github: ktoso
team blog: letitcrash.com
home: akka.io