WEBINAR Understanding Akka Streams, Back Pressure and Asynchronous Architectures by Konrad Malawski (@ktosopl)
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
WEBINAR
Understanding Akka Streams, Back Pressure and Asynchronous Architectures
by Konrad Malawski (@ktosopl)
Konrad `ktoso` Malawski
Akka Team, Reactive Streams TCK,
Persistence, HTTP
Konrad `@ktosopl` Malawski
akka.iotypesafe.comgeecon.org
Java.pl / KrakowScala.plsckrk.com
GDGKrakow.pl lambdakrk.pl
“Stream”
“Stream” What does it mean?!
Akka Streams
Akka Streams && Reactive Streams
Why back-pressure?
?
Why back-pressure?
So you’ve built your app and it’s awesome.
Why back-pressure?
Let’s not smash it horribly under load.
What is back-pressure?
?
What is back-pressure?
No no no…! Not THAT Back-pressure!
No no no…! Not THAT Back-pressure!
What is back-pressure?
Publisher[T] Subscriber[T]
Back-pressure explained
Fast Publisher Slow Subscriber
Push model
Subscriber usually has some kind of buffer.
Push model
Push model
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
Push model
DEMO
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> { }}
Reactive Streams: goals
1) Avoiding unbounded buffering across async boundaries
2)Inter-op interfaces between various libraries
Reactive Streams: goals1) Avoiding unbounded buffering across async boundaries
2) Inter-op interfaces between various libraries
Argh, implementing a correct RS Publisher or Subscriber is so hard!
Reactive Streams: goals1) Avoiding unbounded buffering across async boundaries
2) Inter-op interfaces between various libraries
Argh, implementing a correct RS Publisher or Subscriber is so hard!
Reactive Streams: goals1) 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!
Akka Streams
Streams complement Actors, they do not replace them.
Actors – distribution (location transparency) Streams – back-pressured + more rigid-blueprint
Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
Asynchronous processing toolbox:
Power
Constraints
Akka is a Toolkit, pick the right tools for the job.
Asynchronous processing toolbox:
Constraints
Power
Akka is a Toolkit, pick the right tools for the job.
Single value, no streaming by definition.Local abstraction. Execution contexts.
Power
Constraints
Akka is a Toolkit, pick the right tools for the job.
Mostly static processing layouts.Well typed and Back-pressured!
Constraints
Power
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
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
Akka Streams
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(???).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 in 20 seconds:
// types: _Source[Int, Unit] Flow[Int, String, Unit] Sink[String, Future[String]]
Source.single(1).map(_.toString).runWith(Sink.head)
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)
What is “materialization” really?
What is “materialization” really?
What is “materialization” really?
What is “materialization” really?
Akka Streams & HTTP
streams& HTTP
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.
Streaming in Akka HTTPDEMO
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]
Streaming in Akka HTTPDEMO
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, _]
Streaming in Akka HTTPDEMO
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]
Persistence Query (experimental)
“The Query Side” of Akka Persistence
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.
Akka + Kafka = BFF
Reactive Kafka
+
Started by Krzysiek Ciesielski & Adam Warski @ SofwareMill.com
“ACKnowladged streams”
happy ACKing!
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.
Kafka + Akka = BFF
Kafka + Akka = BFF
Streams talking to Actors &&
Actors talking to Streams
Streams <=> Actors inter-op
Source.actorRef (no back-pressure) Source.queue (safe)
Sink.actorRef (no back-pressure) Sink.actorRefWithAck (safe)
Exciting times ahead!
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.
Upgrade your grey matterTwo free O’Reilly eBooks by Lightbend
DOWNLOADNOWDOWNLOADNOW
lightbend.com/pov
Reactive RoundtableWorld Tour by Lightbend
lightbend.com/reactive-roundtable
Proof of Value ServiceAccelerate Project Success
Q/A
ktoso @ lightbend.com twitter: ktosopl
github: ktosoteam blog: letitcrash.com
home: akka.io
Related Documents
