Zen of Akka

Konrad `@ktosopl` Malawski @ Tokyo, Scala Matsuri 2016

の zen

Konrad `ktoso` Malawski

(we’re renaming soon!)

Akka Team, Reactive Streams TCK, Maintaining Akka Http

Konrad `@ktosopl` Malawski

akka.iotypesafe.comgeecon.org

Java.pl / KrakowScala.plsckrk.com / meetup.com/Paper-Cup @ London

GDGKrakow.pl lambdakrk.pl

(we’re renaming soon!)

はじめまして!

Who are you guys?

Why such talk?

1番: One actor is no Actor

2番: Structure your Actors

3番: Name your Actors

4番: ”Matrix of mutability (Pain)”

5番: Blocking needs careful management

6番: Never Await, for/flatMap instead!

7番: Avoid Java Serialization

7.5番: Trust no-one, benchmark everything!

Agenda

8番: Let it Crash!

9番: Backoff Supervision

10番: Design using State Machines

11番: Cluster Convergence and Joining

12番: Cluster Partitions and “Down”

13番: Akka is a Toolkit.

14番: Happy Hakking, Community!

Questions?

“The Tao / Zen of Programming”

Talk title loosely based on the “Tao of Programming” book

by Goeffrey James (1987).

「プログラミングの Tao (道)」

And the follow-up book“Zen of Programming”.

「プログラミングの Zen (禅)」の 2冊に影響を受けたトーク

Available here: http://www.mit.edu/~xela/tao.html

Series of nine “books”, stories about an apprentice programmer and his sensei.

Thus spake the Master Programmer:

“Without the wind, the grass does not move. Without software hardware is useless.”

師が弟子に語る形式の 9冊シリーズの一部「風無くば草は揺れず。ソフト無くばハードは無用の長物。」

The Akka landscape

Akka Actor IO Cluster Cluster Tools (PubSub, Sharding, …) Persistence & Persistence Query Streams HTTP Typed

1つのアクターは、0個のアクター

アクターは他のアクターと協調するためにあるアクターは一つの責務に特化するべき

If you have only one actor then it can only…

1. Reply2. Drop the message (“ignore”3. Schedule another message to self

So we’re not really making any use of itsparallelism or concurrency capabilities.

- Actors are meant to work together.- An Actor should do one thing and do it very well- then talk to other Actors to do other things for it.

- Child Actors usually used for workers or “tasks” etc.

- Avoid using `actorSelection`, introduce Actors to each other.

アクターはエンティティーを表すそれぞれが「一貫性の島」であるべき

Different types…but no structure!

Parent / child relationshipsalso allow for Actor supervision.

3番: Name your Actors // default context.actorOf(childProps) // "$a", "$b", "$c"

Default names are: BASE64(sequence_nr++)Here’s why: - cheap to generate - guarantees uniqueness - less chars than plain numbers

3番: Name your Actors // default: naming is BASE64(sequential numbers) context.actorOf(childProps) // "$a", "$b", "$c"

// better: but not very informative... context.actorOf(childProps, nextFetchWorkerName) // "fetch-worker-1", "fetch-worker-2"

private var _fetchWorkers: Int = 0 private def nextFetchWorkerName: String = { _fetchWorkers += 1 s”fetch-worker-${_fetchWorkers}” }

Sequential names are a bit better sometimes.

// better: but not much informative... context.actorOf(childProps, nextFetchWorkerName) // "fetch-worker-1", "fetch-worker-2"

private var _fetchWorkers: Int = 0 private def nextFetchWorkerName: String = { _fetchWorkers += 1 s”fetch-worker-${_fetchWorkers}” }

abstract class SeqActorName { def next(): String def copy(name: String): SeqActorName}object SeqActorName { def apply(prefix: String) = new SeqActorNameImpl(prefix, new AtomicLong(0))}

final class SeqActorNameImpl(val prefix: String, counter: AtomicLong) extends SeqActorName { def next(): String = prefix + '-' + counter.getAndIncrement()

def copy(newPrefix: String): SeqActorName = new SeqActorNameImpl(newPrefix, counter)}

If you use this pattern a lot, here’s a simple encapsulation of it:

// better: but not much informative... context.actorOf(childProps, nextFetchWorkerName) // "fetch-worker-1", "fetch-worker-2"

private var fetchWorkers: Int = 0 private def nextFetchWorkerName: String = { fetchWorkers += 1 s"fetch-worker-$fetchWorkers" }

// BEST: proper names, based on useful information context.actorOf(childProps, fetcherName(videoUrl)) // "fetch-yt-MRCWy2E_Ts", ... def fetcherName(link: Link) = link match { case YoutubeLink(id, metadata) => s"fetch-yt-$id" case DailyMotionLink(id, metadata) => s"fetch-dm-$id" case VimeoLink(id, metadata) => s"fetch-vim-$id" }

Meaningful names are the best!

アクターが適切に命名すべきアクターの名前は一意で、内容と構造を表す

import akka.actor.OneForOneStrategyimport akka.actor.SupervisorStrategy._import scala.concurrent.duration._

// ... extends Actor with ActorLogging {

override def supervisorStrategy: SupervisorStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1.minute) { case ex: Exception ⇒ log.warning("Child {} failed with {}, attempting restart...", sender().path.name, ex.getMessage) Restart }

The name of the failed child Actor!

import akka.actor.OneForOneStrategyimport akka.actor.SupervisorStrategy._import scala.concurrent.duration._

// ... extends Actor with ActorLogging {

override def supervisorStrategy: SupervisorStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1.minute) { case ex: Exception ⇒ log.warning("Child {} failed with {}, attempting restart...", sender().path.name, ex.getMessage) Restart }

The name of the failed child Actor! // BAD –– String ALWAYS built log.debug(s"Something heavy $generateId from $physicalAddress") // GOOD! –– String built only when DEBUG level is ON log.debug("Something heavy {} from {}", generateId, physicalAddress)

Side note: always use {} log formatting (or macros), not s””

See Jamie Allen’s talk on the subject.

さまざまな可変性ミュータブル＋値よりも、イミュータブル＋変数

正しくは「ブロッキングは注意深く管理すべき」バルクヘッド（防水隔壁）パターンが有効

Blocking operations are really bad.Actors are all about resource sharing, and if someone is “behaving badly” it hurts everyone.

Here is an example how blocking can grind an app to a halt.

Next we’ll see how to avoid that… even if we have to live with the blocking code.

ブロック操作を使ってはいけないアクター同士が共有するリソースを独り占めすると皆が困る

In simple terms:

Blocking is bad because instead of doing something else,we just wait and do nothing (wasting CPU time)…

ブロック操作を使ってはいけないアクター同士が共有するリソースを独り占めすると皆が困る

Future でブロックしている悪い例デフォルトのディスパッチャーが枯渇する

Having that said, it’s not a bad question. Let’s investigate.

5番: Blocking needs careful management// 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 } }}

5番: Blocking needs careful management// 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 } }}

// application.conf

my-blocking-dispatcher { type = Dispatcher executor = “thread-pool-executor"

thread-pool-executor { // in Akka previous to 2.4.2: core-pool-size-min = 16 core-pool-size-max = 16 max-pool-size-min = 16 max-pool-size-max = 16

// or in Akka 2.4.2+ fixed-pool-size = 16 } throughput = 100 }

5番: Blocking needs careful management// GOOD (due to the blocking on a dedicated dispatcher):implicit val blockingDispatcher = system.dispatchers.lookup("my-blocking-dispatcher")

val routes: Route = post { complete { Future { // uses the good "blocking dispatcher" that we configured, // instead of the default dispatcher – the blocking is isolated. Thread.sleep(5000) System.currentTimeMillis().toString } }}

Future でブロックしている良い例別のディスパッチャーに隔離

The “Never block!” mantra sounds cool,but actually what we mean by it is “blocking needs careful management”.

We use the “bulkhead” pattern separate out potentially blockingbehaviours to their independent dispatchers (and should always do so).

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

6番: Never Await, for/flatMap instead!

6番: Never Await, for/flatMap instead! // ... extends Actor { import context.dispatcher import scala.concurrent.duration._ import scala.concurrent.Await // bad sign!

// BAD!!! val fThings: Future[Things] = computeThings() val t: Things = Await.result(fThings, atMost = 3.seconds) val d: Details = Await.result(moreDetailsFor(t), atMost = 3.seconds)

Await してはいけない代わりに map 関数を使う

// Good: val fThingsWithDetails = for { t <- computeThings() d <- moreDetailsFor(t) } yield t -> d

fThingsWithDetails foreach { case (things, details) => // case (things: Things, details: Details) => println(s"$things with $details") }

// Good: val fThingsWithDetails = for { t <- computeThings() d <- moreDetailsFor(t) } yield t -> d

fThingsWithDetails foreach { case (things, details) => // case (things: Things, details: Details) => println(s"$things with $details") }

// adding timeout: val timeoutFuture = akka.pattern.after(3.seconds, context.system.scheduler) { Future.failed(new TimeoutException("My timeout details...")) }

Future.firstCompletedOf(fThingsWithDetails :: timeoutFuture :: Nil) foreach { case (things, details) => // case (things: Things, details: Details) => println(s"$things with $details") }

Java Serialization is the default one in Akka, since it’s easy to get started with it – no configuration needed.

If you need performance and are running on multiple nodes, you must change the serialization.

Popular formats are ProtoBuf or Kryo.

Kryo is easier, but harder to evolve schema with.ProtoBuf is harder to maintain but great schema evolution.

Java Serialization は遅い性能を出したければ、ProtoBuf か Kryo

7番: Avoid Java SerializationBenchmarking serialization impact on “ping pong” case.

(Two actors sending a message between them.)

in-process messaging, super fast. no serialization overhead.

more work => increased latency => decreased throughput.

over-the-network messaging,slower due to network and serialization.

Java Serialization is known to be:very slow & footprint heavy

It is on by default in Akka… Why?a) zero setup => simple to “play around”b) historical reasons - hard to remove the default

Since 2.4 a warning is logged:

WARNING: Using the default Java serializer for class [{}] which is not recommended because of performance implications. Use another serializer or disable this warning using the setting 'akka.actor.warn-about-java-serializer-usage'

7番: Avoid Java Serializationsbt> jmh:run -f 1 -tu us -wi 20 -i 10 -jvm /home/ktoso/opt/jdk1.8.0_65/bin/java -jvmArgsAppend -XX:+PreserveFramePointer -bm avgt .*pingPong.*

[info] # JMH 1.10.3 (released 184 days ago, please consider updating!) [info] # VM version: JDK 1.8.0_65, VM 25.65-b01 [info] # VM invoker: /home/ktoso/opt/jdk1.8.0_65/bin/java [info] # VM options: -XX:+PreserveFramePointer [info] # Warmup: 20 iterations, 5 s each [info] # Measurement: 10 iterations, 1 s each [info] # Timeout: 10 min per iteration [info] # Threads: 1 thread, will synchronize iterations [info] # Benchmark mode: Average time, time/op [info] # Benchmark: akka.actor.ForkJoinActorBenchmark.pingPong [info] # Parameters: (serializer = java)

github.com/ktoso/sbt-jmhopenjdk.java.net/projects/code-tools/jmh/

7番: Avoid Java Serialization[info] # Warmup Iteration 1: 35.717 us/op . . . [info] # Warmup Iteration 19: 25.164 us/op [info] # Warmup Iteration 20: 23.862 us/op [info] Iteration 1: 25.790 us/op . . . [info] Iteration 10: 26.168 us/op [info] Result "pingPong": [info] 25.464 ±(99.9%) 1.175 us/op [Average] [info] (min, avg, max) = (24.383, 25.464, 26.888), stdev = 0.777 [info] CI (99.9%): [24.289, 26.639] (assumes normal distribution)

[info] ForkJoinActorBenchmark.pingPong java avgt 10 25.464 ± 1.175 us/op [info] ForkJoinActorBenchmark.pingPong off avgt 10 0.967 ± 0.657 us/op

github.com/ktoso/sbt-jmhopenjdk.java.net/projects/code-tools/jmh/

Good serializers include (but are not limited to): Kryo, Google Protocol Buffers, SBE,Thrift, JSON even (sic!)

// dependencies"com.github.romix.akka" %% "akka-kryo-serialization" % "0.4.0"

// application.confextensions = [“com.romix.akka.serialization.kryo.KryoSerializationExtension$"]serializers { java = "akka.serialization.JavaSerializer" kryo = "com.romix.akka.serialization.kryo.KryoSerializer" }

akka.actor.serialization-bindings { “com.mycompany.Example”: kryo . . .}

[info] ForkJoinActorBenchmark.pingPong java avgt 10 25.464 ± 1.175 us/op [info] ForkJoinActorBenchmark.pingPong kryo avgt 10 4.348 ± 4.346 us/op [info] ForkJoinActorBenchmark.pingPong off avgt 10 0.967 ± 0.657 us/op

----sr--model.Order----h#-----J--idL--customert--Lmodel/Customer;L--descriptiont--Ljava/lang/String;L--orderLinest--Ljava/util/List;L--totalCostt--Ljava/math/BigDecimal;xp--------ppsr--java.util.ArrayListx-----a----I--sizexp----w-----sr--model.OrderLine--&-1-S----I--lineNumberL--costq-~--L--descriptionq-~--L--ordert--Lmodel/Order;xp----sr--java.math.BigDecimalT--W--(O---I--scaleL--intValt--Ljava/math/BigInteger;xr--java.lang.Number-----------xp----sr--java.math.BigInteger-----;-----I--bitCountI--bitLengthI--firstNonzeroByteNumI--lowestSetBitI--signum[--magnitudet--[Bxq-~----------------------ur--[B------T----xp----xxpq-~--xq-~--

Java Serialization

final case class Order(id: Long, description: String, totalCost: BigDecimal, orderLines: ArrayList[OrderLines], customer: Customer)

<order id="0" totalCost="0"><orderLines lineNumber="1" cost="0"><order>0</order></orderLines></order>XML…!

{"order":{"id":0,"totalCost":0,"orderLines":[{"lineNumber":1,"cost":0,"order":0}]}}JSON…!

------java-util-ArrayLis-----model-OrderLin----java-math-BigDecima---------model-Orde-----Kryo…!

Excellent post by James Sutherland @ http://java-persistence-performance.blogspot.com/2013/08/optimizing-java-serialization-java-vs.html

7番: Avoid Java Serialization for Persistence!!!

Java Serialization is a horrible idea if you’re going to store the messages for a long time.

For example, with Akka Persistence we store events “forever”.

Use a serialization format that can evolve over time in a compatible way. It can be JSON or ProtocolBuffers (or Thrift etc).

Java Serialization を永続化に使わないスキーマの変更しやすいフォーマットを選ぶべき

7.5番: Trust no-one, benchmark everything!

Always measure and benchmark properly before judging performance of a tool / library.

Benchmarking is often very hard,use the right tools: - JMH (for Scala via: ktoso/sbt-jmh)- YourKit / JProfiler / …- Linux perf_events

7.5番

推測するな、計測せよベンチマークを書いてプロファイラでスレッドの挙動を診断

8番: Let it Crash! Supervision, Failures & Errors

http://www.reactivemanifesto.org/

Error … which is an expected and coded-for condition—for example an error discovered during input validation, that will be communicated to the client …

Failure… is an unexpected event within a service that prevents it from continuing to function normally. A failure will generally prevent responses to the current, and possibly all following, client requests.

8番: Let it Crash! Supervision, Failures & 「エラー」はビジネスロジックの問題「障害」はインフラの問題

「エラー」はビジネスロジックの問題「障害」はインフラの問題

Error: “Not enough cash.”

Error: “Unable to fulfil request”Failure: “Row 3 is broken”

Our goal is to “let things crash”and “recover gracefully”

Not to hammer the DB while it tries to recover!

クラッシュさせた上で、しなやかな回復を目指す

クラスタでは BackoffSupervisor

全アクターが同時に再起動し DB に殺到するのを防ぐ

9番: Backoff SupervisionIF we allowed immediate restarts…

we could end up in “infinite replay+fail hell”.

(we don’t. since Persistence went stable in 2.4.x)

Many PersistentActors Fail and Stop.

Backoff Supervisor counts failuresand starts “the same” entity after exponential timeouts.

def receive = { case Thingy() => // ... case AnotherThingy() => // ... case DoOtherThings() => // ... case PleaseGoAway() => // ... case CarryOn() => // ... case MakeSomething() => // ... // ... }

有限状態機械を作るFSM トレイトか become メソッドを使う

Good: Actors avoid the “pyramid of doom”.

Pyramid of doom in someasync programming styles.

That well works because“everything is a message”:

10番: Design using State Machines def receive = awaitingInstructions

def awaitingInstructions: Receive = terminationHandling orElse { case CarryOn() => // ... case MakeSomething(metadata) => // ... context become makeThings(meta) }

def makeThings(metadata: Metadata): Receive = terminationHandling orElse { case Thingy() => // make a thingy ... case AnotherThingy() => // make another thingy ... case DoOtherThings(meta) => // ... context become awaitingInstructions }

def terminationHandling: Receive = { case PleaseGoAway() => // ... context stop self }

DoOtherThings

MakeSomething

10番: Design using State MachinesWe also provide an FSM (Finite State Machine) helper trait.

You may enjoy it sometimes, give it a look.

DoOtherThings

MakeSomething

http://doc.akka.io/docs/akka/2.4.1/scala/fsm.html

class Buncher extends FSM[State, Data] { startWith(Idle, Uninitialized) when(Idle) { case Event(SetTarget(ref), Uninitialized) => stay using Todo(ref, Vector.empty) } // transition elided ... when(Active, stateTimeout = 1 second) { case Event(Flush | StateTimeout, t: Todo) => goto(Idle) using t.copy(queue = Vector.empty) } // unhandled elided ... initialize()}

http://doc.akka.io/docs/akka/2.4.1/common/cluster.html

Cluster Gossip Convergence

When a node can prove that the cluster state it is observinghas been observed by all other nodes in the cluster.

Cluster Gossip Convergence When a node can prove that the cluster state it is observing

has been observed by all other nodes in the cluster.

Convergence is required for “Leader actions”,which include Join-ing and Remove-ing a node.

Down-ing can happen without convergence.

akka.cluster.allow-weakly-up-members=on

Everyone has ‘seen’ Kurt joining,

move him to Up.

Kurt is now with usin the Cluster.

I can’t hear Kurt…He’s unreachable…

Kurt does has not seen Rei… I can not

mark him as Up!

allow-weakly-up-members=off // default

I’ll mark Rei as “WeaklyUp” until Kurt

is back.

allow-weakly-up-members=on

allow-weakly-up-members

Kurt is back! Once he has seen Rei I’ll mark

Rei as Up.

allow-weakly-up-members=on

allow-weakly-up-members

I’m going

home…

Make sure the others have heard you say goodbye before you leave.

Vanishes immediately.

Make sure the others have heard you say goodbye before you leave.

“Where’s Bill? I did not hear him say Goodbye!”

“Failure Detector” used to determine UNREACHABLE.

Failure detection only triggers “UNREACHABLE”.Nodes can come back from that state.

Declaring DOWN is done by either timeouts (which is rather unsafe) [auto-downing].Or by “voting” or “majority” among the members of the cluster [split-brain-resolver].

お前は

もう死んでいる

Node declared “DOWN” comes back…

お前は

Why do we do that?In order to guarantee consistency via “single writer principle”.

Note: Akka Distributed Data has no need for “single writer”, it’s CRDT based. But it’s harder to model things as CRDT, so it’s a trade off.

Notice that we do not mention “Quarantined”.That is a state in Akka Remoting, not Cluster.It’s a terminal state from which one can never recover.

TL;DR;use Akka Cluster instead of Remoting.it’s pretty much always the thing you need (better than remoting).

13番: A fishing rod is a Tool. Akka is a Toolkit.

Akka strives is Toolkit,not a Framework.

“Give a man a fish and you feed him for a dayteach a man to fish and you feed him for a lifetime.”

13番: Akka is a Toolkit, pick the right tools for the job.

“Constraints Liberate, Liberties Constrain”

Runar Bjarnason

Runar’s excellent talk @ Scala.World 2015

The less powerful abstraction must be built on top of

more powerful abstractions.

Asynchronous processing toolbox:

Akka は道具箱。正しい道具を選ぶべし。レゴを組み合わせるようにしてアプリを作れる

Single value, no streaming by definition.Local abstraction. Execution contexts.

Mostly static processing layouts.Well typed and Back-pressured!

Plain Actor’s younger brother, experimental.Location transparent, well typed.Technically unconstrained in actions performed

Location transparent. Various resilience mechanisms.

(watching, persistent recovering, migration, pools)

Untyped and unconstrained in actions performed.

Akka it a Toolkit. Not a Framework.

Another example is persisting data.Akka Persistence is specifically geared towards Event Sourcing.

If you know you you want a raw Key-Value Store and do not need Event Sourcing’s capabilities, don’t use Akka Persistence – it would be the wrong tool for the job.

If you use it for Event Sourcing though… it’ll work very well for you.There it is the right tool for the job.

用途に合わない道具は使うべきではない例えば CQRS が適切でない時は生 DB アクセスを使えば済む

14番: Happy hAkking, Community!

人にはやさしく

コミュニティーに積極的に参加する

14番: Happy hAkking, Community!

akka.io – websitegithub.com/akka/akka/issues – help out!

“community-contrib” or “small” for starters

groups.google.com/group/akka-user – mailing list

gitter.im/akka/akka – chat about using Akkagitter.im/akka/dev – chat about developing Akka

Links• http://stackoverflow.com/questions/34641861/akka-http-blocking-

in-a-future-blocks-the-server/34645097#34645097• The wonderful Zen paintings to buy here:

http://paintingwholesalechina.com/products/chinese-culture-zen-no-1-academic-chinese-painting

• http://doc.akka.io/docs/akka/2.4.1/common/cluster.html• http://www.mit.edu/~xela/tao.html• http://doc.akka.io/docs/akka/2.4.1/common/cluster.html• http://doc.akka.io/docs/akka/2.4.1/scala/cluster-usage.html• akka.io et al.

https://www.typesafe.com/products/typesafe-reactive-platform

Certified builds & Long Term Support

Advanced commercial features:Monitoring, Split Brain Resolver, ConductR,

Play User Quotas, Play SOAP

Reactive Platform

ありがとう!

ktoso @ typesafe.com twitter: ktosopl

github: ktosoteam blog: letitcrash.com

home: akka.io

Thus spake the Master Programmer:

“After three days without programming,life becomes meaningless.”

Q/A (Now’s the time to ask things!)

ktoso @ typesafe.com twitter: ktosopl

github: ktosoteam blog: letitcrash.com

home: akka.io

Zen of Akka

Internet

Zen of Akka

Open Source Used In Stealthwatch Visibility Assessment App 1 · 1.17 akka-http-core_2.12 10.0.11 1.18 akka-http-spray-json_2.12 10.0.11 1.19 akka-http_2.12 10.0.11 1.20 akka-parsing_2.12

2010 AKKA NEWSLETTER Volume 3, Issue 1torakenryu.com/images/2010AKKAnewsletter.pdf · AKKA in Hungary AKKA in Europe June: AKKA Camp Volume 3, Issue 1 T he past year was a difficult

Introducing Akka

Akka JUGL 2012

Effective akka scalaio

A Tour of (Advanced) Akka Features in 40 Minutes - doag.org · Akka Features in 40 Minutes Johan Janssen, Info Support @johanjanssen42. Content Why Akka? Local actor Remote actor

Basics of Akka

Akka and the Zen of Reactive System Design

Akka 101 - durtchrt.github.io · Akka Actor ActorRef, mailbox, actor, dispatcher 이미지 출처: akka in action. Akka Actor Path Identifies an Actor May also represent a proxy

AKKA Technologies AKKA Technologies développe de multiples ...images.autodesk.com/emea_s_main/files/akka_technologies.pdf · AKKA Technologies s’est aussi servi de Showcase pour

AKKA TECHNOLOGIES

Akka framework

AKKA BUSINESS UNIT GERMANY - akka-technologies.com · akka group business unit germany grÜndung von akka research grÜndung des ersten unternehmens 1984 1999 2007 2010 2011 2012

Akka stream

Akka streams

Kiki, principal enterprise architect @Lightbend · Akka Streams Akka Cluster Akka Http. Scalability Cube: 3 dimensions of scalability Y-axis: scale via functional compositi on x-axis:

Akka Documentation - doc.akka.io · Akka is a very scalable piece of software, not only in the performance sense, but in the size of applications it is useful for. The core of Akka,

AKKA - ondernemeninantwerpen.be · AKKA AKKA is one of Europe’s leading engineering and technology consulting companies (N°1 in mobility). With 16,500 skilled employees and a solid

Akka patterns