Akka 2.4 plus new commercial features in Typesafe Reactive Platform

Akka 2.4.x
and
Commercial Features
1 September 2015
Akka 2.4 and RP commercial features
with Konrad `@ktosopl` Malawski

Konrad `ktoso` Malawski
Akka Team,
Reactive Streams TCK

Reactive Platform: Akka
• Reactive Platform: Versioning explained
• Remoting / Cluster: Docker networking support
• Cluster: Split Brain Resolver (beta)
• Akka Persistence: Cross-Scala-version snapshot deseriali
• Java 6: Extended LTS
• Akka 2.4.x (currently in Release Candidate phase)
• Cluster Tools promoted to stable!
• Persistence promoted to stable!
• Persistence Queries (experimental)
• Akka Typed (experimental)
• Distributed Data (experimental)
• Akka Streams (currently 1.0, will be included in 2.4.x eventually)
• Q / A

Reactive Platform

Reactive Platform
https://together.typesafe.com/products/reactivePlatform

Reactive Platform Versioning
15v05p01
year
month
patch
=
Scala 2.10.x
Akka 2.3.x
…

15v05p01
year
month
patch
=
Scala 2.10.x
Akka 2.3.x
…
15v09p01 =
Scala 2.11.x
Akka 2.3.12 => 2.3.++ (binary compatible!)
…

How does this help?
• Huge integration test suite before release
• Certified to work well together
• Priority updates / additional features
• Back-ports, fixes, long-term support
Life cycle of each release: 2 years

NAT Support

support
(NAT support)

support
(NAT support)
Available in:
RP 15v01p## (Akka 2.3.x),
OSS only in Akka 2.4.x (work in progress)

Docker / NAT support
docker run -d -p 8000:2551 akka-app
Akka App
172.17.0.11:2551
Outside world
example.com:8000
doc.akka.io/docs/akka/2.4.0-RC1/general/remoting.html#Peer-to-Peer_vs__Client-Server

Docker / NAT support
akka.remote {
netty.tcp {
hostname = example.com # external (logical) hostname
port = 8000 # external (logical) port
bind-hostname = 172.17.0.11 # internal (bind) hostname
bind-port = 2552 # internal (bind) port
}
}
doc.akka.io/docs/akka/2.4.0-RC1/general/remoting.html#Peer-to-Peer_vs__Client-Server

Split Brain Resolver

Split Brain Resolver (customer beta)
Installing the Reactive Platform with Split Brain Resolver

• Fundamental Problem in all distributed systems
• SBR helps to make decisions, is not a magic wand
• A set of pre-built strategies
for when to down nodes in a cluster.
• Strategies:
• Static Quorum (like zoo-keeper)
• Keep Majority
• Keep Oldest
• Keep Referee
http://doc.akka.io/docs/akka/rp-15v09p01/scala/split-brain-resolver.html

• The default behaviour of Akka Cluster is “Manual Downing”:
• A node needs to issue cluster.down(address)
• This decision can be powered by external monitoring,
or DevOps teams observing the cluster.
• It is Safe.
however involves the most human/automated work
• A naive implementation exists called “auto-downing”
• It is not very safe to be used in real clusters
• Definitely not a good choice for apps using Persistence
• It is not recommended for production deployments.
http://doc.akka.io/docs/akka/rp-15v09p01/scala/split-brain-resolver.html

Heartbeats
A

Heartbeats
A
everyone is down!

Heartbeats
A
`n-1` is down!
I’ll take over `A`!

Heartbeats
A
`n-1` is down!
I’ll take over `A`!
A
good if: n-1 really is down.
bad: if n-1 is just very unresponsive
Fundamentally, it is hard to distinguish the two states in distributed systems.

Static Quorum (3 (> (n/2 +1))
A

Static Quorum (3 (> (n/2 +1))
we need to down
ourselves
A

Keep Majority (aka. dynamic quorum)
A

Keep Majority (aka. dynamic quorum)
A
we need to down
ourselves

referee node
Keep Referee
A
down-all-if-less-than-nodes

referee node
Keep Referee
A
can’t see referee node!
down-all-if-less-than-nodes

oldest node
Keep Oldest
A
can’t see oldest node!
down-if-alone
oldest node can change,
if “up until now oldest node” leaves the
cluster.
This is more dynamic than keep-referee.

Persistence
Cross-scala-version
compatibility for snapshots

Persistence: Cross-scala-version compat
Enhanced compatibility for persistent snapshot envelope.
(Payload compatibility depends on application.)

• Scala 2.10 is not binary compatible with 2.11
• 10 => 11 is “major update” by design

• More compatible deserialisation than the language
itself!
• Eases migration to latest Scala versions
• (Your journal may actually not be affected and be compatible
anyway.)
• Scala 2.10 is not binary compatible with 2.11
• 10 => 11 is “major update” by design

saveSnapshot( )
Akka 2.3 + Scala 2.10

saveSnapshot( )
loadSnapshot

Java 6
Extended LTS

Java 6: Extended LTS
• Existing RP releases keep Java 1.6 compatibility

• Akka Streams & Http currently work with 1.6
• will be merged into Akka 2.4 (likely after 1.1)
• we can back-port and support for RP (Akka 2.3)

• Akka Streams & Http currently work with 1.6
• will be merged into Akka 2.4 (likely after 1.1)
• we can back-port and support for RP (Akka 2.3)
• Akka 2.4 does require Java 8
• Scala 2.11 + Scala 2.12 (once released)
• it will enter RP once released

Akka 2.4

Akka 2.4
• 2.3 => 2.4 is a major update
• additional effort made it binary compatible!
• it does require Java 1.8
• i.e. update your JDK, same sources “just work”
• Future:
• Version bump to 3.0 if binary incompatible

Cluster Tools
Now Stable!

Akka 2.4 Cluster Tools (Now Stable!)
• Distributed Pub Sub
• Smart message batching (1 send per node)
• Cluster Singleton
• useful for “master” or coordinator Actors
• Cluster Sharding
• useful for sharding load onto the cluster
• graceful shut-down
• balancing load across cluster

doc.akka.io/docs/akka/2.4.0-RC1/scala/distributed-pub-sub.html
class Subscriber extends Actor with ActorLogging {
import DistributedPubSubMediator.{ Subscribe, SubscribeAck }
val mediator = DistributedPubSub(context.system).mediator
// subscribe to the topic named "content"
mediator ! Subscribe("content", self)
def receive = {
case SubscribeAck(Subscribe("content", None, `self`)) ⇒
context become ready
}
def ready: Actor.Receive = {
case s: String ⇒
log.info("Got {}", s)
}
}
Akka 2.4 Cluster Tools - PubSub (Now Stable!)

class Publisher extends Actor {
import DistributedPubSubMediator.Publish
// activate the extension
val mediator = DistributedPubSub(context.system).mediator
def receive = {
case in: String ⇒
val out = in.toUpperCase
mediator ! Publish("content", out)
}
}
doc.akka.io/docs/akka/2.4.0-RC1/scala/distributed-pub-sub.html
Akka 2.4 Cluster Tools - PubSub (Now Stable!)

A
B
C D
http://doc.akka.io/docs/akka/2.4.0-RC1/scala/cluster-singleton.html
Akka 2.4 Cluster Tools - Cluster Singleton (Now Stable!)

A
B
C
D
role: backend-2 role: backend-2
http://doc.akka.io/docs/akka/2.4.0-RC1/scala/cluster-singleton.html
Akka 2.4 Cluster Tools - Cluster Singleton (Now Stable!)

Akka 2.4 Cluster Tools - Sharding (Now Stable!)
A B
C
D
doc.akka.io/docs/akka/2.4.0-RC1/scala/cluster-sharding.html

Akka 2.4 Cluster Tools - Sharding (Now Stable!)
val counterRegion: ActorRef = ClusterSharding(system).start(
typeName = "Counter",
entityProps = Props[Counter],
settings = ClusterShardingSettings(system),
extractEntityId = extractEntityId,
extractShardId = extractShardId)
val counterRegion: ActorRef =
ClusterSharding(system).shardRegion("Counter")
counterRegion ! Get(123)
expectMsg(0)
Start a shard region:
Other nodes in cluster send messages to it:
doc.akka.io/docs/akka/2.4.0-RC1/scala/cluster-sharding.html

Persistence
Now Stable!

var state = S0
persistenceId = “a”
Command
Journal
Akka 2.4 Persistence (Now Stable!)

var state = S0
Journal
Generate Events

var state = S0
Journal
Generate Events
E1

ACK “persisted”
Journal
E1
var state = S0

“Apply” event
Journal
E1
var state = S0
E1

Journal
E1
var state = S0
E1
Okey!

Journal
E1
var state = S0
E1
Ok, he got my $.

…sum of states…
Journal
E1 E2 E3 E4
E5 E6 E7 E8

Snapshot Store
snapshot!
state until [E5]
S5Journal
E1 E2 E3 E4
E5 E6 E7 E8

Snapshot Store
state until [E8]
S8Journal
E1 E2 E3 E4
E5 E6 E7 E8
S5

state until [E8]
S8
Snapshot Store
Journal
E1 E2 E3 E4
E5 E6 E7 E8
crash!
S5

Snapshot Store
Journal
E1 E2 E3 E4
E5 E6 E7 E8
crash!
S5

“bring me up-to-date!”
Snapshot Store
Journal
E1 E2 E3 E4
E5 E6 E7 E8
restart!
replay!
S5

“bring me up-to-date!”
Snapshot Store
restart!
replay!
S5
Journal
E1 E2 E3 E4
E5 E6 E7 E8
S5

state until [E8]
Snapshot Store
S5
restart!
replay!
S8
Journal
E1 E2 E3 E4
E5 E6 E7 E8

Advanced support for long-term schema evolution.
EventAdapters & StringManifestSerializers

Persistence Query
New module, experimental!

Akka 2.4 Persistence Queries (experimental)
API still work in progress, should be polished very soon.
val query: Source[RichEvent, QueryStats] =
readJournal.query(ByTagsWithStats(Set("red", "blue")))
query
.map { event => s"Event payload: ${event.payload}" }
.runWith(Sink.ignore)

Akka 2.4 Persistence Queries (experimental)
API still work in progress, should be polished very soon.
val query: Source[RichEvent, QueryStats] =
readJournal.query(ByTagsWithStats(Set("red", "blue")))
query
.map { event => s"Event payload: ${event.payload}" }
.runWith(Sink.ignore)
Journalwrite-side query-side
Query
Optimised
Journal
Simple view / result

Akka Typed

doc.akka.io/docs/akka/2.4.0-RC1/scala/typed-actors.html
class Greeter extends Actor {
def receive = {
case msg: Greet ⇒
println(s"Hello ${msg.whom}!")
sender() ! Greeted(msg.whom)
}
}
val system: ActorSystem =
ActorSystem(“akka-actor-system“)
val greeter =
system.actorOf(Props[Greeter])
system ! Greet("kapi", system.deadLetters)
Akka Actor
Akka 2.4 Typed (experimental)

val greeter = Total[Greet] { msg ⇒
msg.replyTo ! Greeted(msg.whom)
Same
}
val system: ActorSystem[Greet] =
ActorSystem(“typed", Props(totalGreeter))
def receive = {
case msg: Greet ⇒
}
}
val greeter =
Akka Actor Akka Typed

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
The main concept is Behaviour[T]
Explicit protocols for the win!

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
Since Behaviour[Greet] is typed,
msgis-a Greet.

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
The Behaviour[T]is the receive.

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
sender()is no more.

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
sender()is no more.
final case class Greet(whom: String, replyTo: ActorRef[Greeted])

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
ActorRef[T]is now typed!

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
become()is required.
And replaced by returning the “next” Behaviour[T]
// context.become(receive)

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
On a conceptual level at least,
we provide Static[T]if you don’t need become.

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
Special behaviors:
Same / Unhandled / Empty / Stopped / Ignore

Same
}
def receive = {
case msg: Greet ⇒
}
}
val greeter =
“Root actor” is not user-defined for ActorSystem[T]
Encourages thinking about supervision.

Akka Distributed Data

Akka 2.4 Distributed Data (experimental)
for “small, fast data”
Implements various CRDTs and gossips changes through the
Cluster.
CRDTs = Conflict-free Replicated Data Types
Data structures that can be concurrently updated
and always eventually converge on the same value.
doc.akka.io/docs/akka/2.4.0-RC1/scala/distributed-data.html

value = 5
n1: 2
n2: 0
n3: 3
Akka Cluster (gossip)
GCounter example:
value = 5
n1: 2
n2: 0
n3: 3
value = 5
n1: 2
n2: 0
n3: 3
node-1 node-2
node-3

value = 6
n1: 2+1
n2: 0
n3: 3
GCounter example:
value = 5
n1: 2
n2: 0
n3: 3
value = 6
n1: 2
n2: 0
n3: 3+1
node-1 node-2
node-3

node-1 node-2
node-3
value = 7
n1: 2+1
n2: 0
n3: 3+1
GCounter example:
value = 5
n1: 2
n2: 0
n3: 3
value = 6
n1: 2
n2: 0
n3: 3+1

node-1 node-2
node-3
value = 7
n1: 2+1
n2: 0
n3: 3+1
GCounter example:
value = 7
n1: 2+1
n2: 0
n3: 3+1
value = 7
n1: 2+1
n2: 0
n3: 3+1

implicit val node = Cluster(system)
val replicator = DistributedData(system).replicator
val Counter1Key = PNCounterKey("counter1")
replicator ! Update(Counter1Key, PNCounter(), WriteLocal)(_ + 1)

Provided data types:
• Counters: GCounter, PNCounter
• Sets: GSet, ORSet
• Maps: ORMap, ORMultiMap, LWWMap, PNCounterMap
• Registers: LWWRegister, Flag

Akka Streams & Http
1.0 (experimental) currently.
will join the Akka 2.4.x release.

Akka Streams & Http (experimental, to eventually join Akka 2.4.x)
Akka Streams
Asynchronous Back-pressured Stream Processing
An implementation of Reactive Streams.
Reactive Streams
an initiative co-lead by Typesafe, Netflix, Pivotal, RedHat),
allowing for inter-op of asynchronous streaming libraries.
Akka Http
Superseeds Spray.io, the well known high-performance Http serv
Fully based on Akka Streams.
http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0/scala.html

http://www.reactive-streams.org/
Reactive Streams (1.0, en route to be included in JDK9)

Akka Streams <-> Actors – Advanced
val subscriber = ActorSubscriber(
system.actorOf(Props[SubStreamParent], ”parent”)
)
Source(1 to 100)
.map(_.toString)
.filter(_.length == 2)
.drop(2)
.conflate(seed => seed)((acc, i) => acc + i)
.groupBy(_.last)
.runWith(subscriber)
All the usual ops available for Linear Flows.
http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0/scala.html

Links
• akka.io
• reactive-streams.org
• akka-user
• https://www.typesafe.com/subscription
• Streams and Http
• http://doc.akka.io/docs/akka-stream-and-http-
experimental/1.0/scala.html
• http://doc.akka.io/docs/akka-stream-and-http-
experimental/1.0/java.html
• Akka 2.4 RC1
• http://doc.akka.io/docs/akka/2.4.0-RC1/scala.html
• http://doc.akka.io/docs/akka/2.4.0-RC1/java.html

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