scalaswarm 2017 keynote: tough this be madness yet theres method in't

Though this be madnessyet there’s method in’t

Konrad `ktoso` Malawski @ ScalaSwarm @ Porto 2017

Hamlet quote

Samurai

Hamlet quote

Samurai

Hamlet quoteARGH! THIS SLIDE DOES NOT MAKE ANY SENSE!

Samurai

Hamlet quote

or does it…?

ARGH! THIS SLIDE DOES NOT MAKE ANY SENSE!

“Though this be madnessyet there’s method in’t”

Quote of Polonius,Act II, Scene IIHamlet, 1602

by William Shakespeare

“Throne of Blood”Akira Kurosawa, 1957 Transposes plot of Macbeth

(by Shakespeare) to Feudal Japan

Also adapted Hamlet, in “The Bad sleep Well” however screenshot would not contrast so well…

Welcome to

Let’s get in the mood of:in-depth learning and understanding.

Welcome to

Let’s get in the mood of:in-depth learning and understanding.

Instead of: having a “quick superficial quick look” at things.

Konrad `ktoso` Malawski

Akka Team,Reactive Streams TCK,

Scala SLIP Committee member

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

The concurrent & distributed applications toolkit

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

A thing about nulls

"I call it my billion-dollar mistake." Sir C. A. R. Hoare, on his invention of the null reference

A thing about nulls

something.calculateSum(2, 2)

What does this code do?

A thing about nulls

something.calculateSum(2, 2)

What does this code do?

a) return 4b) NullPointerException!c) System.exit(0) // though I have a love-hate relationship with this answer…

The curious case of OptionsScala Option – 2007 (Scala 2.5, sic! http://www.scala-lang.org/old/node/165)Guava Optional – 2011 (since v10.0)Java Optional – 2014 (since v1.8)

Scala Option – 2007 (Scala 2.5, sic! http://www.scala-lang.org/old/node/165)Guava Optional – 2011 (since v10.0)Java Optional – 2014 (since v1.8)

The curious case of Options

sealed abstract class Option[+A] extends Product with Serializable { self =>

def isEmpty: Boolean def isDefined: Boolean = !isEmpty def get: A @inline final def getOrElse[B >: A](default: => B): B = if (isEmpty) default else this.get

Scala Option – 2007 (Scala 2.5, sic! http://www.scala-lang.org/old/node/165)Guava Optional – 2011 (since v10.0)Java Optional – 2014 (since v1.8)

sealed abstract class Option[+A] extends Product with Serializable { self =>

def isEmpty: Boolean def isDefined: Boolean = !isEmpty def get: A @inline final def getOrElse[B >: A](default: => B): B = if (isEmpty) default else this.get

public final class Optional<T> { public boolean isPresent() { return value != null; } public T get() { if (value == null) throw new NoSuchElementException("No value present"); return value; } public T orElseGet(Supplier<? extends T> other) { return value != null ? value : other.get(); }

val o: Option[String] = ??? o.foreach(_.toUpperCase(Locale.ROOT)) // ok, sure o match { case Some(value) => value.toUpperCase(Locale.ROOT) case None => "_"}

We all have the same “mistake”: get seems innocent, but it’s not…

final Optional<String> optional = Optional.of("");

optional.map(it -> it.toUpperCase(Locale.ROOT)); if (optional.isPresent()) { optional.get().toUpperCase();}

Can we do better than that though?

“What the eyes don’t see,the programmer does not invoke.”

We hide the “bad” API somewhere…where it’s a bit harder to reach?

Analysis: Scala Future APIs

Separated API?

Blocking is the new “you broke the build!”

Blocking is the new “you broke the build!”// BAD! (due to the blocking in Future):implicit val defaultDispatcher = system.dispatcher

val routes: Route = post { complete { Future { // uses defaultDispatcher

Thread.sleep(5000) // will block on the default dispatcher, System.currentTimeMillis().toString // starving the routing infra } }}

Blocking is the new “you broke the build!”// BAD! (due to the blocking in Future):implicit val defaultDispatcher = system.dispatcher

val routes: Route = post { complete { Future { // uses defaultDispatcher

Thread.sleep(5000) // will block on the default dispatcher, System.currentTimeMillis().toString // starving the routing infra } }}

http://stackoverflow.com/questions/34641861/akka-http-blocking-in-a-future-blocks-the-server/34645097#34645097

The curious case of Futures

The curious case of Futurespublic class CompletableFuture<T> implements Future<T>, CompletionStage<T> {

public T get() throws InterruptedException, ExecutionException { // ... }

public T get(long timeout, TimeUnit unit) throws InterruptedException, ExecutionException, TimeoutException { // ... }

The curious case of Futures

Anyone remember the days beforescala.concurrent.Future?

Back to the Future, in which we discuss Akka and Twitter Futures in 2012 :-)https://groups.google.com/forum/?fromgroups=#!topic/akka-user/eXiBV5V7ZzE%5B1-25%5D

trait Future[+T] extends Awaitable[T] {

// THERE IS NO GET! // Closest thing to it is...

def value: Option[Try[T]] // However it’s not that widely known actually // Notice that it is non-blocking!

object Await {

@throws(classOf[TimeoutException]) @throws(classOf[InterruptedException]) def ready[T](awaitable: Awaitable[T], atMost: Duration): awaitable.type = blocking(awaitable.ready(atMost)(AwaitPermission))

@throws(classOf[Exception]) def result[T](awaitable: Awaitable[T], atMost: Duration): T = blocking(awaitable.result(atMost)(AwaitPermission)) }

Any => Unit

It’s not like we never thought about Types.

Any => Unit

It’s not like we never thought about Types.(like… 3+ years of developing Akka Streams)

Any => Unit

The journey to Akka TypedOne actor is no Actor

The Actor model is strictly about:

1. React on message2. Send finite number of messages3. Spawn child Actor

def receive: Any => Unit = { case WorkNow(id) => sender() ! “okey!” context.become(working(id))}

The journey to Akka Typed

The journey to Akka TypedAncient API, deprecated“Typed Actor” API

Goal was to expose what Java developers knew.

Old “TypedActor” experimental in 2.3, removed

Upsides:- Easily bridge to “non-Akka” / “non-Reactive” apps- type-safe- “easy” (not necessarily a good thing)

Downsides:- Reflection, 10x slow-down compared to UntypedActor- “RPC”-ish, not true to the core messaging- Not true to Akka’s core principle: Messaging

“Typed Channels” experimental in 2.3, removed

Upsides:- completely type-safe- very expressive

Downsides:- Too complex, many new operators- Had to rely on scala macros - “sender” difficult to solve

http://axel22.github.io/resources/docs/reactors.pdf

Akka Typed

try it now, 2.5.2

2 styles, 100% awesome.Full Java & Scala API, as usual.

Actor.mutable – similar to current Actors, Behavior is a classActor.immutable – more functional style, recommended

Akka Typed

Main user-facing changes:

ActorRef[T] typed ActorRefs.

Core concept is Behavior[T]which can be freely composed.

You always “become(Behavior)”, by returning Behavior.

sender() is gone,not possible to type it well.

sender was trouble anyway, so that’s good!

Akka Typed

Untyped

Actor.mutable

Akka Typed

Untyped

Akka TypedActor.immutable

Akka TypedActor.immutable (Scala)

Don’t worry, Java will eventually get pattern matching:http://mail.openjdk.java.net/pipermail/amber-spec-experts/2017-April/000033.html

Java adopting Scala features confirms Scala’s design.

…but, until then we provide you with helpers and DSLs:

Akka TypedActor.immutable (Scala)

Actor.immutable (Java)

Akka Typed

try it now, 2.5.2

Learn more:from the docs:http://doc.akka.io/docs/akka/snapshot/scala/typed.html

and the blog:1.Akka Typed: Hello World in the new API2.Akka Typed: Coexistence3.Akka Typed: Mutable vs. Immutable4.Akka Typed: Protocols

5. Akka Typed: Supervision6.Akka Typed: Lifecycle and Watch7.Akka Typed: Timers

What would happen if we never mutated things?

Never ever "change”

”Matrix of mutability (Pain)”

https://xkcd.com/1312/

See Jamie Allen’s talk on the subject.

Append-only is a way of life.Never change & never delete

Append-only is a lifestyle

Only ever append “facts”

More facts, write more facts…!

Whoops, we ran out of space!

Thankfully, unlike stone tablets, disk is cheap…

There is no “delete”.There is no “update”.

Those are Writes too.

Why would anyone do this?!

(Event sourcing).andThen(CQRS)

Receive commands.Store events.Optional: Create queries / views

API design always comes with some context.

What’s in an API?

“Squiggle Kingdom”

What do you think this API was designed for?What’s the context?

“Squiggle kingdom”

What do you think this API was designed for?What’s the context?

He certainly overdid it there :-)

“Squiggle Kingdom”“Squiggle kingdom”

An API to look like Graphs drawn on whiteboard.

BidiFlow.fromGraph(GraphDSL.create() { implicit b => import GraphDSL.Implicits._ val netIn = new Inlet[ByteString]("netIn") val netOut = new Outlet[ByteString]("netOut") val httpIn = new Inlet[HttpRequest]("httpIn") val httpOut = new Outlet[HttpResponse]("httpOut") httpIn <~ requestPrep <~ controller.requestOut; controller.requestIn <~ requestParsing <~ netIn httpOut ~> controller.responseIn; controller.responseOut ~> rendererPipeline ~> netOut BidiShape(netIn, netOut, httpIn, httpOut)})

Very rarely used “super-power-user” API.

Normally just stick to:

val adding = Flow[Int].map(_ + 1) val source = Source.maybe[Int].via(adding).map(_ - 1) source.runWith(Sink.foreach(println))

Very rarely used “super-power-user” API.

Normally just stick to:

val adding = Flow[Int].map(_ + 1) val source = Source.maybe[Int].via(adding).map(_ - 1) source.runWith(Sink.foreach(println))

Why did we pick those 3 different words?

Messaging as first-class citizen“The hAkker way”

Messages! Not method calls.

Akka Actor in one sentence:

Messaging as a core abstraction, not slap-on afterthought.

Akka in one sentence:

A toolkit for building highly distributed and concurrent apps.

http://c2.com/cgi/wiki?AlanKayOnMessaging

Waldo J, Wyant G, Wollrath A, Kendall S. A @ Sun Microsystems Laboratories. 1994.Note on Distributed Computing

Messages! Not method calls.Methods: // locally:

val value: Long = local.calculateSum(2, 2)// if it’s parallel then we need some middle man to handle concurrency issues hm…

// but remote will have big latency so... val value: Future[Long] = remote.calculateSum(2, 2)// Q1: what is actually put on the wire?// Q2: what about retrying to different host, // - now need magic to handle it...// Q3: can the downstream directly respond to upstream?// - ok, so we could build a special method that does this// Q4: what if the networking breaks...? Do I need to try/catch?

// ... but why, if it could be a simple message send :-)

Messages! Not method calls.Methods: // locally:

val value: Long = local.calculateSum(2, 2)// if it’s parallel then we need some middle man to handle concurrency issues hm…

// but remote will have big latency so... val value: Future[Long] = remote.calculateSum(2, 2)// Q1: what is actually put on the wire?// Q2: what about retrying to different host, // - now need magic to handle it...// Q3: can the downstream directly respond to upstream?// - ok, so we could build a special method that does this// Q4: what if the networking breaks...? Do I need to try/catch?

// ... but why, if it could be a simple message send :-)

Messages: // locally:local ! CalculateSum(2, 2)// I'll get a reply in a bit, or I can retry etc etc.// Actor is can be running in parallel, on multi-core etc, same API.// what can happen?// JVM can crash => effectively message loss

// remotelyremote ! CalculateSum(2, 2)// what can happen?// message loss// receiver of the msg can crash... => message loss

class OneWayProxyActor extends Actor { val downstream: ActorRef = ??? def receive = { case StateQuery(q) => sender() ! run(q)

case req: IncomingRequest => downstream forward transformed(req) } def run(any: Any) = ???}

What do we not see in these scenarios though?

I can totally do the same with REST HTTP calls!

Sure you can (201, 202, 204), but: In Akka that’s both default, and exactly 400bytes cheap.

Messages! Not exposing state.

Encapsulation, so nice…

class ComplexLogic { def add(i: Item): ComplexLogic def apply(): Effect}

But it’s “only for testing”!

getter

class ComplexLogic { def add(i: Item): ComplexLogic def apply(): Effect def state: State}

val logicActor: ActorRef = ???// no way to access state - for your own good.

logicActor ! Add(item)logicActor ! Add(item)logicActor ! ApplyexpectMsgType[StateAppliedSuccessfully]

Test: yes: behaviour and interactions, not: raw state.

One of the 2 hardest problems in computer science!

…and cache invalidation,

Naming things

One of the 2 hardest problems in computer science!

…and cache invalidation, …and 1-off errors.

Naming things

“I want my words back.”Roland Kuhn (former Akka Team lead)

We want our words back…!

Two examples of name-collisions causing confusion:

“Scheduler”&

“Stream”

Naming is hard…

“Scheduler”

“Scheduler” - but is it the right one? class CountdownActor extends Actor { val untilEndOfWorld = 127.days // according to mayan prophecy // oh, great, a scheduler! val scheduler = context.system.scheduler scheduler.scheduleOnce(untilEndOfWorld, self, EndOfTheWorld) def receive = { case EndOfTheWorld => println("Aaaaa!!!") } }

http://doc.akka.io/docs/akka/2.4.4/scala/scheduler.html#scheduler-scala

“Scheduler” - but is it the right one? class CountdownActor extends Actor { val untilEndOfWorld = 127.days // according to mayan prophecy // oh, great, a scheduler! val scheduler = context.system.scheduler scheduler.scheduleOnce(untilEndOfWorld, self, EndOfTheWorld) def receive = { case EndOfTheWorld => println("Aaaaa!!!") } }

Into the Dungeon

Into the akka.actor.dungeon

Technically LARS is not in the dungeon,but couldn’t stop myself from making this reference…

“Scheduler” - but is it the right one?

It’s a plain “Hashed Wheel Timer” though!

Optimised for: • high performance• lockless insertion• O(1) insertion• huge amounts of tasks

// timeouts - on each request, ask timeouts

Original white paper: Hashed and Hierarchical Timing Wheels:https://pdfs.semanticscholar.org/0a14/2c84aeccc16b22c758cb57063fe227e83277.pdf

It’s a plain “Hashed Wheel Timer” though!

Optimised for: • high performance• lockless insertion• O(1) insertion• huge amounts of tasks

// timeouts - on each request, ask timeouts

Not for: • preciseness• persistence

Original white paper: Hashed and Hierarchical Timing Wheels:https://pdfs.semanticscholar.org/0a14/2c84aeccc16b22c758cb57063fe227e83277.pdf

Akka’s Scheduler fun facts!

• Akka impl. after Netty implementation• Akka impl. improved performance quite a bit• Netty pulled-in the optimisations

(just one of multiple (both way) healthy interactions) => Yay, healthy Open Source ecosystem!

Akka’s Scheduler fun facts!Error message you’ll likely never see:LightArrayRevolverScheduler for short: “LARS”

“LARS cannot start new thread, ship’s going down!”

Persistent, reliable job scheduler

Ok, so what do we need for it?

Persistent, reliable job scheduler: Chronos

Ok, so what do we need?

• Persistence• Replication• Consensus on who executes

Optimised for:

• ”Like CRON, but distributed”• Visibility, including management UI• Retries of failed tasks• Far-out tasks (usually measured in days etc)• Consensus on who executes• Also considered Lightweight• 2k lines of Scala• in it’s context… well, it is!

https://www.youtube.com/watch?v=FLqURrtS8IA

Definitely NOT for:

• millions of tasks inserted per second• to be run in the next second

… that’s what the the Akka scheduler is for!

Definitely NOT for:

• millions of tasks inserted per second• to be run in the next second

… that’s what the the Akka scheduler is for!

Notable mention: Quartz (well known long-term scheduler, with Akka integration and persistence)

“Stream”What does it mean?!

* 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”

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)

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。

“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。

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.)

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

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

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

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

A core feature not obvious to the untrained eye…!

Akka Streams / HTTP

Quiz time! TCP is a ______ protocol?

A core feature not obvious to the untrained eye…!

Akka Streams / HTTP

Quiz time! TCP is a STREAMING protocol!

Streaming from Akka HTTPNo demand from TCP

= No demand upstream

= Source won’t generate tweets

Streaming from Akka HTTPNo demand from TCP

= No demand upstream

= Source won’t generate tweets

=> Bounded memory stream processing!

https://vimeo.com/139094998 Kirby Ferguson, 2015 @ https://vimeo.com/139094998

And you can figure out of what exactly

Happy hAkking!

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

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 buildingreactive apps since more than 6 years.

Obligatory “read my book!” slide :-)

Thanks! Questions?

ktoso @ lightbend.comtwitter: ktosopl

github: ktosoteam blog: blog.akka.io

home: akka.iomyself: kto.so

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