Functional Programming and Composing Actors

&√

Deputy CTO

Functional Programming

Composing Actors

&Functional Programming

without sacrificing Communication

Resilience√

Deputy CTO

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Connectedness

«Software is becoming increasingly interconnected»

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Klang’s Conjecture

«If you cannot solve a problem without programming; you cannot solve a problem with programming.»

Reality Imaginary>

My office door

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Stunning views

Reality• separation in space & time gives us

• communication for coordination

• variable delays

• partial failures

• only partial/local/stale knowledge

• systems

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system

«a set of things working together as parts of a mechanism or an interconnecting network;

a complex whole»

noun

Systems• Purpose is typically simple

• Complex inner workings

• Consist of collaborating components

• Components can be systems themselves

• Components are as simple as feasible, but not simpler

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Resilience

«Any sufficiently complex system is always running in degraded mode»

— Richard I. Cook, MD “How complex systems fail” (paraphrased)

Communication• The production and consumption of messages

• Messaging implies that we need to be able to address recipients

• Addresses/Identities are important

• They are knowledge that can be shared

• Messages can become delayed, lost or misunderstood

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Think: Reliability• Are we ever guaranteed delivery?

• at-most-once

• at-least-once

• exactly-once

• It’s not about guarantees,it’s about reliability

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Two-Phased Commit

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Burstiness• Most communication is bursty

• some is predictable

• some is unpredictable

• load shedding can cause burstiness

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Flow control / Back pressure

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• Buffers are only grease between cogs

• Does not solve overload problems

• Load shedding does not inform sender

• Reasons

• When shedding will end

www.reactive-‐streams.org

Resilience• Never assume that other entities are immortal

• Treat expectation violations as failures

• Always have a Plan B

• Clients are not responsible to fix a faulty provider

• Fail fast & predictably

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Supervision• Responsibility to deal with the failure/corruption of other

• Does not place the burden of fixing it on the collaborators

«Quis custodiet ipsos custodes?» — Decimus Iunius Iuvenalis

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actors

• Akka's unit of computation is called an actor • Akka actors are purely reactive components: • Current behavior • Address • Local storage • Mailbox

• Scheduled to execute when sent a message • An actor has a parent actor, handling its failures • An actor may have 0..N child actors

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« 2500 nodes × millions of actors per GB RAM = a lot»

actors

• An actor processes a message at a time • Multiple-producers & Single-consumer

• The overhead per actor is about ~450bytes • Run millions of actors on commodity hardware

• Akka Cluster currently handles ~2500 nodes

actors

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actors

• Great for • Communication • Location transparency • Elasticity

• Resilience • Main challenge • Composition

Functional Compositiona → b → c

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• Great for • Composition

• Main challenge • Communication • Resilience

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typed: On the horizon

• Project Gålbma

• distill an Actor to its essence: the Behavior

• everything is a message—for real this time

• remove the danger to close over Actor environment

• behavior composition

• completely pure Actor implementation,through a process algebra (inspired by Join Calculus)

Behavior is King

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abstract class Behavior[T] { def management(ctx: ActorContext[T], msg: Signal): Behavior[T]

def message(ctx: ActorContext[T], msg: T): Behavior[T]

def narrow[U <: T]: Behavior[U] = this.asInstanceOf[Behavior[U]]}

Behavior example

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object Server { sealed trait Command case class Get(id: Int)(val replyTo: ActorRef[Got]) extends Command case class Put(name: String, ref: ActorRef[OtherCommand]) extends Command

case class Got(id: Int, contents: Map[String, ActorRef[OtherCommand]])

val initial: Behavior[Command] = withMap(Map.empty)

private def withMap(map: Map[String, ActorRef[OtherCommand]]) = Total[Command] { case g @ Get(id) => g.replyTo ! Got(id, Map.empty) Same case Put(name, ref) => withMap(map.updated(name, ref)) }}

Most common dev task?

• Receive inputs

• Transform data

• Produce outputs

• Drink coffee

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#undef STREAMS #define STREAMS• ephemeral, time-dependent, sequences of elements

• possibly unbounded in length

• in essence: transformation & transportation of data

«No man ever steps in the same river twice, for it's not the same river

and he's not the same man.» — Heraclitus

streams

i m m u t a b l e

REUSABLE c o m p o s a b l e c o o r d i n a t e d asynchronoustransformations

Getting data across an asynchronous b o u n d a r y

Getting data across an asynchronous b o u n d a r y with non-blockingback pressure

Reactive Streams Initiative

T H E

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Requirements Push Pull

support potentially unbounded sequences 😃 😃

sender runs separately from receiver 😃 😃

rate of reception may vary from rate of sending 😃 😃

dropping elements should be a choice and not a necessity 💩 😃

minimal (if any) overhead in terms of latency and throughput 😃 💩

Comparing Push vs Pull

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Supply

Demand

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Publisher Subscriber

data

demand

• “push” when subscriber is faster

• “pull” when publisher is faster

• switches automatically between both

• batching demand allows batching ops

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DynamicPush–Pull

Reactive Streams

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Requirements Push Pull Both

support potentially unbounded sequences 😃 😃 😃

sender runs separately from receiver 😃 😃 😃rate of reception may vary from rate of sending 😃 😃 😃

dropping elements should be a choice and not a necessity 💩 😃 😃

minimal (if any) overhead in terms of latency and throughput 😃 💩 😃

Comparing Push vs Pull vs Both🌟

Stream splitting

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demand

data

splitting the data means merging the demand

Stream merging

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merging the data means splitting the demand

Source Flow Sink

RunnableGraph

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streams: Linear transformations

val fives = Source.repeat(5)

val timesTwo = Flow[Int].map(_ * 2)

val intToString = Flow[Int].map(_.toString)

val transform = timesTwo via intToString

val sysOut = Sink.foreach(println)

val r = fives via transform.take(10) to sysOut

r.run() // a Materializer needs to be in scope

BidiFlows

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streams: Selected BidiFlows Examples

val codec: BidiFlow[Foo, ByteString, ByteString, Foo, Unit] = …val crypto:BidiFlow[ByteString, ByteString, ByteString, ByteString, Unit] = …val framing: BidiFlow[ByteString, ByteString, ByteString, ByteString, Unit] = …val protocol = codec atop crypto atop framing

DAG

DCGWAT

Fan-In

Fan-Out&

Fan-tastic!

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streams: Cthulhu-Merge-Route Explained

OI

Materialization

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streams: Materialization

• Akka Streams separates the what from the how & when

• declarative Source/Flow/Sink DSL to create a blueprint

• ActorMaterializer turns this into running Actors

• enables customizable materialization strategies

• optimization

• verification / validation

• distributed deployment

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streams: MaterializedValues

• At Composition time

• MaterializedValues of respective sides are composed

• At Materialization time

• A value of the final type of the MaterializedValueof the graph is returned

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streams: Composition

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val runnableGraph = FlowGraph.closed() { implicit builder => import FlowGraph.Implicits._

val A: Outlet[Int] = builder.add(Source.single(0)) val B: UniformFanOutShape[Int, Int] = builder.add(Broadcast[Int](2)) val C: UniformFanInShape[Int, Int] = builder.add(Merge[Int](2)) val D: FlowShape[Int, Int] = builder.add(Flow[Int].map(_ + 1)) val E: UniformFanOutShape[Int, Int] = builder.add(Balance[Int](2)) val F: UniformFanInShape[Int, Int] = builder.add(Merge[Int](2)) val G: Inlet[Any] = builder.add(Sink.foreach(println)) C <~ F A ~> B ~> C ~> F B ~> D ~> E ~> F E ~> G }

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Akka Streams

Akka Typed


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streams: Future?


Akka Streams

Akka Typed


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Summary

Akka Streams and Akka Typed promises both the compositional strengths of FP, paired with the communicational strengths and resilience of the Actor Model.

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Opportunity: Self-tuning back pressure

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• Each processing stage can know • Latency between requesting more and getting more • Latency for internal processing • Behavior of downstream demand • Latency between satisfying and receiving more • Trends in requested demand (patterns)

• Lock-step • N-buffered • N + X-buffered • “chaotic”

Opportunity: Operation elision

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• Compile-time, using Scala Macros • fold ++ take(n where n > 0) == fold • drop(0) == identity • <any> ++ identity == <any>

• Run-time, using intra-stage simplification • map ++ dropUntil(cond) ++ take(N) • map ++ identity ++ take(N) • map ++ take(N)

Opportunity: Operation fusion

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• Compile-time, using Scala Macros • filter ++ map == collect

• Run-time, using intra-stage simplification • Rule: <any> ++ identity == <any>

Rule: identity ++ <any> == <any> • filter ++ dropUntil(cond) ++ map • filter ++ identity ++ map == collect

Opportunity: Execution optimization

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• synchronous intra-stage execution N steps then trampoline and/or give control to other Thread / Flow

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public interface Publisher<T> { public void subscribe(Subscriber<T> s); } public void Subscription { public void request(long n); public void cancel(); } public interface Subscriber<T> { public void onSubscribe(Subscription s); public void onNext(T t); public void onError(Throwable t); public void onComplete(); } public interface Processor<T, R> extends Subscriber<T>, Publisher<R> { }

How does it connect?

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SubscriberPublisher

subscribe(subscriber)

onSubscribe(subscription)

How does data flow?

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SubscriberPublisher

subscription.request(1)

onNext(element)


onNext(element)


How does data flow?

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SubscriberPublisher

onNext(element)

onNext(element)


onNext(element)

onNext(element)

How does it complete?

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SubscriberPublisher


onNext(element)

onComplete()

onNext(element)

What if it fails?

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SubscriberPublisher


onNext(element)


onError(exception)

☠

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Try Akka Streams: (1.0)https://github.com/typesafehub/activator-akka-stream-scala

References

Reactive Streams for JVMhttps://github.com/reactive-streams/reactive-streams-jvm

https://github.com/typesafehub/activator-akka-stream-scala

https://github.com/typesafehub/activator-akka-stream-scala