In order to be successful with asynchronous programming, when coming from synchronous execution models you need to change your mindset and look at things from a slightly different perspective. In order to use Akka at it's best, you will have to change the way you think about application design (loosen coupling in space and time between components), and re-think what you've maybe learned in the past. In this talk we uncover a number of rules that serve as a guide in designing concurrent distributed applications, how those apply to Akka, and how they can help you in daily app development. Aimed at developers through architects, Akka team happy hAkker, Konrad Malawski, bends your parameters with regards to application design and asynchronous execution models.

1. Akka and the Zen of Reactive
System Design
by Konrad Malawski (@ktosopl)

3. Konrad `@ktosopl` Malawski
akka.io
typesafe.com
geecon.org
Java.pl / KrakowScala.pl
sckrk.com / meetup.com/Paper-Cup @ London
GDGKrakow.pl
lambdakrk.pl

4. Why such talk?
1 : One actor is no Actor
2 : Structure your Actors
3 : Name your Actors
4 : ”Matrix of mutability (Pain)”
5 : Blocking needs careful management
6 : Never Await, for/flatMap instead!
7 : Avoid Java Serialization
7.5 : Trust no-one, benchmark everything!
Agenda
8 : Let it Crash!
9 : Backoff Supervision
10 : Design using State Machines
11 : Cluster Convergence and Joining
12 : Cluster Partitions and “Down”
13 : Akka is a Toolkit.
14 : Happy Hakking, Community!
Questions?

6. “The Tao / Zen of Programming”
Talk title loosely based on
the “Tao of Programming” book
by Goeffrey James (1987).

7. “The Tao / Zen of Programming”
And the follow-up book
“Zen of Programming”.

8. “The Tao / Zen of Programming”
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.”

9. The Akka landscape

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

11. The Zen of Akka
Is best explained as a way of thinking about Architecture.
Akka provides building blocks, with specific semantics.

12. The Zen of Akka
Is best explained as a way of thinking about Architecture.
Akka provides building blocks, with specific semantics.
Actors are cheap – so they can be 1:1 for a user, or wallet etc
Actors are referentially transparent – can scale-out trivially
Actors encapsulate state – avoiding global state
Actors are engines –
Streams / Streaming HTTP / Cluster Sharding / Distributed Data…
– all using are Actors as engines, high-level Architectural help.

13. 1 : One actor is no Actor

14. 1 : One actor is no Actor

15. 1 : One actor is no Actor
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.

16. 1 : One actor is no Actor

17. 1 : One actor is no Actor

18. 1 : One actor is no Actor

19. 1 : One actor is no Actor

20. 1 : One actor is no Actor

21. 1 : One actor is no Actor
- 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.

22. 2 : Structure your Actors

23. 2 : Structure your Actors
Different types…
but no structure!

24. 2 : Structure your Actors
Parent / child relationships
also allow for Actor supervision.

25. 2 : Structure your Actors

26. 3 : Name your Actors

27. 3 : 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

28. 3 : 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.

29. 3 : Name your Actors
// default: naming is BASE64(sequential numbers)
context.actorOf(childProps) // "$a", "$b", "$c"
// better: but not much informative...
context.actorOf(childProps, nextFetchWorkerName) // "fetch-worker-1", "fetch-worker-2"
private var _fetchWorkers: Int = 0
private def nextFetchWorkerName: String = {
_fetchWorkers += 1
s”fetch-worker-${_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:

30. 3 : Name your Actors
// default: naming is BASE64(sequential numbers)
context.actorOf(childProps) // "$a", "$b", "$c"
// better: but not much informative...
context.actorOf(childProps, nextFetchWorkerName) // "fetch-worker-1", "fetch-worker-2"
private var fetchWorkers: Int = 0
private def nextFetchWorkerName: String = {
fetchWorkers += 1
s"fetch-worker-$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!

31. 3 : Name your Actors
Meaningful names are the best!
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!

32. 3 : Name your Actors
Meaningful names are the best!
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!
// 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””

33. 4 : ”Matrix of mutability (Pain)”

34. 4 : ”Matrix of mutability (Pain)”
See Jamie Allen’s talk on the subject.

35. 4 : ”Matrix of mutability (Pain)”
See Jamie Allen’s talk on the subject.

36. 4 : ”Matrix of mutability (Pain)”
See Jamie Allen’s talk on the subject.

37. 4 : ”Matrix of mutability (Pain)”
See Jamie Allen’s talk on the subject.

38. 4 : ”Matrix of mutability (Pain)”
See Jamie Allen’s talk on the subject.

39. 4 : ”Matrix of mutability (Pain)”

40. 5 : Blocking needs careful management

41. 5 : Blocking needs careful management
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.

42. 5 : Blocking needs careful management
In simple terms:
Blocking is bad because instead of doing something else,
we just wait and do nothing (wasting CPU time)…

43. 5 : Blocking needs careful management

44. 5 : Blocking needs careful management
Having that said, it’s not a bad question. Let’s investigate.

45. 5 : 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
}
}
}

46. 5 : 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
}
}
}

47. 5 : Blocking needs careful management
// 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
}

48. 5 : 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
}
}
}

49. 5 : Blocking needs careful management
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

50. 6 : Never Await, for/flatMap instead!

51. 6 : 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)

52. 6 : 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)
// 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")
}

53. 6 : 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)
// 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")
}

54. 7 : Avoid Java Serialization

55. 7 : Avoid Java Serialization
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.

56. 7 : Avoid Java Serialization
Benchmarking serialization impact on “ping pong” case.
(Two actors sending a message between them.)
in-process messaging, super fast.
no serialization overhead.

57. 7 : Avoid Java Serialization
more work => increased latency => decreased throughput.
over-the-network messaging,
slower due to network and serialization.

58. 7 : Avoid Java Serialization
Java Serialization is known to be:
very slow & footprint heavy

59. 7 : Avoid Java Serialization
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'

60. 7 : Avoid Java Serialization
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/

61. 7 : 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-jmh
openjdk.java.net/projects/code-tools/jmh/

62. 7 : Avoid Java Serialization
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

63. 7 : Avoid Java Serialization
----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

64. 7 : 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).

65. 7.5 : 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

66. 8 : Let it Crash! Supervision, Failures & Errors

67. http://www.reactivemanifesto.org/
8 : Let it Crash! Supervision, Failures & Errors
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.

68. http://www.reactivemanifesto.org/
8 : Let it Crash! Supervision, Failures &

69. http://www.reactivemanifesto.org/
8 : Let it Crash! Supervision, Failures & Errors

70. http://www.reactivemanifesto.org/
8 : Let it Crash! Supervision, Failures & Errors
Error: “Not enough cash.”

71. http://www.reactivemanifesto.org/
8 : Let it Crash! Supervision, Failures & Errors
Error: “Unable to fulfil request”
Failure: “Row 3 is broken”

72. 9 : Backoff Supervision

73. 9 : Backoff Supervision

74. 9 : Backoff Supervision

75. 9 : Backoff Supervision

76. 9 : Backoff Supervision
Our goal is to “let things crash”
and “recover gracefully”
Not to hammer the DB while it tries to recover!

77. 9 : Backoff Supervision

78. 9 : Backoff Supervision

79. 9 : Backoff Supervision
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)

80. 9 : Backoff Supervision

81. 9 : Backoff Supervision
Many PersistentActors Fail and Stop.

82. 9 : Backoff Supervision

83. 9 : Backoff Supervision
Backoff Supervisor counts failures
and starts “the same” entity after
exponential timeouts.

84. 9 : Backoff Supervision

85. 10 : Design using State Machines

86. 10 : Design using State Machines
def receive = {
case Thingy() =>
// ...
case AnotherThingy() =>
// ...
case DoOtherThings() =>
// ...
case PleaseGoAway() =>
// ...
case CarryOn() =>
// ...
case MakeSomething() =>
// ...
// ...
}

87. 10 : Design using State Machines
def receive = {
case Thingy() =>
// ...
case AnotherThingy() =>
// ...
case DoOtherThings() =>
// ...
case PleaseGoAway() =>
// ...
case CarryOn() =>
// ...
case MakeSomething() =>
// ...
// ...
}
Good:
Actors avoid the “pyramid of doom”.
Pyramid of doom in some
async programming styles.

88. 10 : Design using State Machines
def receive = {
case Thingy() =>
// ...
case AnotherThingy() =>
// ...
case DoOtherThings() =>
// ...
case PleaseGoAway() =>
// ...
case CarryOn() =>
// ...
case MakeSomething() =>
// ...
// ...
}
That well works because
“everything is a message”:

89. 10 : 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

90. 10 : Design using State Machines
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()
}

91. 11 : Cluster Convergence and Joining

92. 11 : Cluster Convergence and Joining
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.

93. 11 : Cluster Convergence and Joining
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.
Convergence is required for “Leader actions”,
which include Join-ing and Remove-ing a node.
Down-ing can happen without convergence.

94. 11 : Cluster Convergence and Joining
http://doc.akka.io/docs/akka/2.4.1/common/cluster.html

95. 11 : Cluster Convergence and Joining
http://doc.akka.io/docs/akka/2.4.1/common/cluster.html
akka.cluster.allow-weakly-up-members=on

96. 11 : Cluster Convergence and Joining

97. 11 : Cluster Convergence and Joining

98. 11 : Cluster Convergence and Joining

99. 11 : Cluster Convergence and Joining

100. 11 : Cluster Convergence and Joining

101. 11 : Cluster Convergence and Joining
Everyone has ‘seen’
Kurt joining,
move him to Up.

102. 11 : Cluster Convergence and Joining
Kurt is now with us
in the Cluster.

103. 11 : Cluster Convergence and Joining
I can’t hear Kurt…
He’s unreachable…

104. 11 : Cluster Convergence and Joining
Kurt does has not
seen Rei… I can not
mark him as Up!
allow-weakly-up-members=off // default

105. 11 : Cluster Convergence and Joining
I’ll mark Rei as
“WeaklyUp” until Kurt
is back.
allow-weakly-up-members=on
allow-weakly-up-members

106. 11 : Cluster Convergence and Joining
Kurt is back! Once he
has seen Rei I’ll mark
Rei as Up.
allow-weakly-up-members=on
allow-weakly-up-members

107. 12 : Cluster Partitions and “Down”

108. 12 : Cluster Partitions and “Down”

109. 12 : Cluster Partitions and “Down”
I’m going
home…

110. 12 : Cluster Partitions and “Down”
Make sure the others have heard you say goodbye before you leave.
Vanishes immediately.

111. 12 : Cluster Partitions and “Down”
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.

112. 12 : Cluster Partitions and “Down”
Failure detection only triggers “UNREACHABLE”.
Nodes can come back from that state.

113. 12 : Cluster Partitions and “Down”
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].

114. 12 : Cluster Partitions and “Down”

115. 12 : Cluster Partitions and “Down”
お
前
は
も
う
死
ん
で
い
る
.
.
.
.
Node declared “DOWN” comes back…

116. 12 : Cluster Partitions and “Down”
You
are
already
dead.

117. 12 : Cluster Partitions and “Down”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.
You
are
already
dead.

118. 12 : Cluster Partitions and “Down”
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).

119. 13 : A fishing rod is a Tool. Akka is a Toolkit.

120. 13 : 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.”

121. 13 : A fishing rod is a Tool. Akka is a Toolkit.
Akka strives is Toolkit,
not a Framework.
Play is a Framework,
Lagom is a Framework.

122. 13 : Akka is a Toolkit, pick the right tools for the job.
“Constraints Liberate,
Liberties Constrain”
Runar Bjarnason
Runar’s excellent talk @ Scala.World 2015

123. 13 : Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
The less powerful abstraction
must be built on top of
more powerful abstractions.

124. 13 : Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
Asynchronous processing toolbox:

125. 13 : Akka is a Toolkit, pick the right tools for the job.
Runar’s excellent talk @ Scala.World 2015
Asynchronous processing toolbox:

126. 13 : Akka is a Toolkit, pick the right tools for the job.
Asynchronous processing toolbox:

127. 13 : Akka is a Toolkit, pick the right tools for the job.
Single value, no streaming by definition.
Local abstraction.
Execution contexts.

128. 13 : Akka is a Toolkit, pick the right tools for the job.
Mostly static processing layouts.
Well typed and Back-pressured!

129. 13 : 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

130. 13 : 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.

131. 13 : Picking the tools – distributing / sharding processing
Runar’s excellent talk @ Scala.World 2015

132. 13 : Picking the tools – distributing / sharding processing
Runar’s excellent talk @ Scala.World 2015Whitepaper by Pat Helland, 2005
http://cidrdb.org/cidr2005/papers/P12.pdf

133. 13 : Picking the tools – “element by element” processing
Runar’s excellent talk @ Scala.World 2015
// types: _
Source[Int, Unit]
Flow[Int, String, Unit]
Sink[String, Future[String]]
Source.single(1).map(_.toString).runWith(Sink.head)

134. 13 : Picking the tools – streaming HTTP APIs

135. 13 : Picking the tools – streaming HTTP APIs

136. 13 : Picking the tools – streaming HTTP APIs

137. 13 : Picking the tools – streaming HTTP APIs

138. No demand from TCP
=
No demand upstream
=
Source won’t generate tweets
13 : Picking the tools – streaming HTTP APIs

139. No demand from TCP
=
No demand upstream
=
Source won’t generate tweets
=>
13 : Picking the tools – streaming HTTP APIs

140. No demand from TCP
=
No demand upstream
=
Source won’t generate tweets
=>
Bounded memory
stream processing!
13 : Picking the tools – streaming HTTP APIs

141. 14 : Happy hAkking, Community!

142. 14 : 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

143. 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.

144. Thanks!
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.”

145. lightbend.com/contact
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World Tour by Lightbend
lightbend.com/reactive-roundtable
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