The Akka Zen: A Talk on Actor Model Best Practices

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.io
typesafe.com
geecon.org
Java.pl / KrakowScala.pl
sckrk.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. Reply
2. Drop the message (“ignore”
3. Schedule another message to self
So we’re not really making any use of its
parallelism or concurrency capabilities.
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

- 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 relationships
also allow for Actor supervision.

// 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

// default: naming is BASE64(sequential numbers)
// 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...
private var _fetchWorkers: Int = 0
_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...
private var fetchWorkers: Int = 0
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.OneForOneStrategy
import 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.OneForOneStrategy
import akka.actor.SupervisorStrategy._
// ... 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

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.

// 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
}

// 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 blocking
behaviours 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!

// ... extends Actor {
import context.dispatcher
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

// BAD!!!
// 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")
}

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

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

Benchmarking 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'

sbt> 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-jmh
openjdk.java.net/projects/code-tools/jmh/

[info] # Warmup Iteration 1: 35.717 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-jmh
openjdk.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.conf
extensions = [“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

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

IF 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 failures
and 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

def receive = {
case Thingy() =>
// ...
// ...
// ...
// ...
case CarryOn() =>
// ...
// ...
// ...
}
Good:
Actors avoid the “pyramid of doom”.
Pyramid of doom in some
async programming styles.

def receive = {
case Thingy() =>
// ...
// ...
// ...
// ...
case CarryOn() =>
// ...
// ...
// ...
}
That well works because
“everything is a message”:

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 ...
// make another thingy ...
case DoOtherThings(meta) =>
// ...
context become awaitingInstructions
}
def terminationHandling: Receive = {
// ...
context stop self
}
DoOtherThings
MakeSomething

We 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 observing
has 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 us
in 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
も
う
死
ん
で
い
る
.
.
.
.

お
前
は
も
う
死
ん
で
い
る
.
.
.
.
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.

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 day
teach 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

Asynchronous processing toolbox:

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 – website
github.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 Akka
gitter.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: ktoso
team 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: ktoso
team blog: letitcrash.com
home: akka.io

The Akka Zen: A Talk on Actor Model Best Practices

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