Post on 19-Aug-2014
description
Building Reactive Applications with Akka
Jonas Bonér Typesafe
CTO & co-founder @jboner
This is an era of profound change.
Reactive Applications
Implications are massive, change is unavoidable
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!!!Users are demanding richer and more personalized experiences. !Yet, at the same time, expecting
blazing fast load time.
Users!!!
Mobile and HTML5; Data and compute clouds; scaling on
demand. !Modern application technologies
are fueling the always-on, real-time user expectation.
Applications !!!Businesses are being pushed to
react to these changing user expectations… !...and embrace
modern application requirements. !!!!!!!
Businesses
As a matter of necessity, businesses are going Reactive.
Reactive Applications
Reactive applications share four traits
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Reactive applications react to changes in the world around them.
Event-Driven• Loosely coupled architecture, easier to extend, maintain, evolve
• Asynchronous and non-blocking • Concurrent by design, immutable state • Lower latency and higher throughput
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“Clearly, the goal is to do these operations concurrently and non-blocking, so that entire blocks of seats or sections are not locked.
We’re able to find and allocate seats under load in less than 20ms without trying very hard to achieve it.”
Andrew Headrick, Platform Architect, Ticketfly
Introducing the Actor Model.
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The Actor Model
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A computational model that embodies:
The Actor Model
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A computational model that embodies:
✓ Processing
The Actor Model
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A computational model that embodies:
✓ Processing
✓ Storage
The Actor Model
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A computational model that embodies:
✓ Processing
✓ Storage
✓ Communication
The Actor Model
9
A computational model that embodies:
✓ Processing
✓ Storage
✓ Communication
Supports 3 axioms—when an Actor receives a message it can:
The Actor Model
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A computational model that embodies:
✓ Processing
✓ Storage
✓ Communication
Supports 3 axioms—when an Actor receives a message it can:
1. Create new Actors
The Actor Model
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A computational model that embodies:
✓ Processing
✓ Storage
✓ Communication
Supports 3 axioms—when an Actor receives a message it can:
1. Create new Actors
2. Send messages to Actors it knows
The Actor Model
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A computational model that embodies:
✓ Processing
✓ Storage
✓ Communication
Supports 3 axioms—when an Actor receives a message it can:
1. Create new Actors
2. Send messages to Actors it knows
3. Designate how it should handle the next message it receives
The Actor Model
The essence of an actor0. DEFINE
1. CREATE
2. SEND
3. BECOME
4. SUPERVISE
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0. DEFINE
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case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s"Hello ${who}") } }
0. DEFINE
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Define the message(s) the Actor should be able to respond to
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s"Hello ${who}") } }
0. DEFINE
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Define the message(s) the Actor should be able to respond to
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s"Hello ${who}") } }
Define the Actor class
0. DEFINE
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Define the message(s) the Actor should be able to respond to
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s"Hello ${who}") } }
Define the Actor class
Define the Actor’s behavior
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
1. CREATE
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
1. CREATE
Create an Actor system
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
1. CREATE
Create an Actor systemActor configuration
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
Give it a name
1. CREATE
Create an Actor systemActor configuration
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
Give it a name
1. CREATE
Create the Actor
Create an Actor systemActor configuration
case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info("Hello " + who) } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
Give it a name
1. CREATE
Create the ActorYou get an ActorRef back
Create an Actor systemActor configuration
Guardian System Actor
Actors can form hierarchies
Guardian System Actor
system.actorOf(Props[Foo], “Foo”)
Actors can form hierarchies
Foo
Guardian System Actor
system.actorOf(Props[Foo], “Foo”)
Actors can form hierarchies
Foo
Guardian System Actor
context.actorOf(Props[A], “A”)
Actors can form hierarchies
A
Foo
Guardian System Actor
context.actorOf(Props[A], “A”)
Actors can form hierarchies
A
B
BarFoo
C
BE
A
D
C
Guardian System Actor
Actors can form hierarchies
A
B
BarFoo
C
BE
A
D
C
Guardian System Actor
Name resolution—like a file-system
A
B
BarFoo
C
BE
A
D
C
/Foo
Guardian System Actor
Name resolution—like a file-system
A
B
BarFoo
C
BE
A
D
C
/Foo
/Foo/A
Guardian System Actor
Name resolution—like a file-system
A
B
BarFoo
C
BE
A
D
C
/Foo
/Foo/A
/Foo/A/B
Guardian System Actor
Name resolution—like a file-system
A
B
BarFoo
C
BE
A
D
C
/Foo
/Foo/A
/Foo/A/B
/Foo/A/D
Guardian System Actor
Name resolution—like a file-system
2. SEND
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case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s”Hello ${who}") } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter") greeter ! Greeting("Charlie Parker")
2. SEND
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case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s”Hello ${who}") } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter") greeter ! Greeting("Charlie Parker")
Send the message asynchronously
Bring it together
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case class Greeting(who: String) !class GreetingActor extends Actor with ActorLogging { def receive = { case Greeting(who) => log.info(s”Hello ${who}") } } !val system = ActorSystem("MySystem") val greeter = system.actorOf(Props[GreetingActor], name = "greeter") greeter ! Greeting("Charlie Parker")
DEMO TIMEA simple game of ping pong
3. BECOME
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class GreetingActor extends Actor with ActorLogging { def receive = happy ! val happy: Receive = { case Greeting(who) => log.info(s”Hello ${who}") case Angry => context become angry } ! val angry: Receive = { case Greeting(_) => log.info("Go away!") case Happy => context become happy } }
3. BECOME
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class GreetingActor extends Actor with ActorLogging { def receive = happy ! val happy: Receive = { case Greeting(who) => log.info(s”Hello ${who}") case Angry => context become angry } ! val angry: Receive = { case Greeting(_) => log.info("Go away!") case Happy => context become happy } }
Redefine the behavior
Reactive applications are architected to handle failure at all levels.
Resilient• Failure is embraced as a natural state in the app lifecycle
• Resilience is a first-class construct • Failure is detected, isolated, and managed • Applications self heal
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“The Typesafe Reactive Platform helps us maintain a very aggressive development and deployment cycle, all in a fail-forward manner.
It’s now the default choice for developing all new services.”
Peter Hausel, VP Engineering, Gawker Media
Think Vending Machine
Coffee MachineProgrammer
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Add more coins
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Gets coffee
Add more coins
Think Vending Machine
Coffee MachineProgrammer
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Think Vending Machine
Out of coffee beans error
Coffee MachineProgrammer
Inserts coins
Think Vending Machine
Out of coffee beans errorWrong
Coffee MachineProgrammer
Inserts coins
Think Vending Machine
Coffee MachineProgrammer
Inserts coins
Out of coffee beans
error
Think Vending Machine
Coffee MachineProgrammer
Service Guy
Inserts coins
Out of coffee beans
error
Think Vending Machine
Coffee MachineProgrammer
Service Guy
Inserts coins
Out of coffee beans
error
Adds more beans
Think Vending Machine
Coffee MachineProgrammer
Service Guy
Inserts coins
Gets coffee
Out of coffee beans
error
Adds more beans
Think Vending Machine
The Right Way
ServiceClient
The Right Way
ServiceClient
Request
The Right Way
ServiceClient
Request
Response
The Right Way
ServiceClient
Request
Response
Validation Error
The Right Way
ServiceClient
Request
Response
Validation Error
Application Error
The Right Way
ServiceClient
Supervisor
Request
Response
Validation Error
Application Error
The Right Way
ServiceClient
Supervisor
Request
Response
Validation Error
Application Error
Manages Failure
• Isolate the failure
• Compartmentalize
• Manage failure locally
• Avoid cascading failures
Use Bulkheads
• Isolate the failure
• Compartmentalize
• Manage failure locally
• Avoid cascading failures
Use Bulkheads
Enter Supervision
Enter Supervision
A
B
BarFoo
C
BE
A
D
C
Automatic and mandatory supervisionSupervisor hierarchies
4. SUPERVISE
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Every single actor has a default supervisor strategy.
Which is usually sufficient. But it can be overridden.
4. SUPERVISE
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Every single actor has a default supervisor strategy.
Which is usually sufficient. But it can be overridden.
class Supervisor extends Actor { override val supervisorStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1 minute) { case _: ArithmeticException => Resume case _: NullPointerException => Restart case _: Exception => Escalate } ! val worker = context.actorOf(Props[Worker], name = "worker") ! def receive = {
4. SUPERVISE
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class Supervisor extends Actor { override val supervisorStrategy = OneForOneStrategy(maxNrOfRetries = 10, withinTimeRange = 1 minute) { case _: ArithmeticException => Resume case _: NullPointerException => Restart case _: Exception => Escalate } ! val worker = context.actorOf(Props[Worker], name = "worker") ! def receive = { case n: Int => worker forward n } } !
Cleanup & (Re)initialization
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class Worker extends Actor { ... override def preRestart( reason: Throwable, message: Option[Any]) { ... // clean up before restart } override def postRestart(reason: Throwable) { ... // init after restart } }
Monitor through Death Watch
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class Watcher extends Actor { val child = context.actorOf(Props.empty, "child") context.watch(child) ! def receive = { case Terminated(`child`) => … // handle child termination } }
Reactive applications scale up and down to meet demand.
Scalable• Scalability and elasticity to embrace the Cloud
• Leverage all cores via asynchronous programming • Clustered servers support joining and leaving of nodes • More cost-efficient utilization of hardware
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“Our traffic can increase by as much as 100x for 15 minutes each day. Until a couple of years ago, noon was a stressful time.
Nowadays, it’s usually a non-event.”
Eric Bowman, VP Architecture, Gilt Groupe
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Scale OUTScale UP
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Essentially the same thing
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1. Minimize Contention 2. Maximize Locality of Reference
We need to
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Share NOTHING
Design
Fully event-driven apps are a necessity
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Amdahl’s Law will hunt you down
Define a router
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val router = context.actorOf( RoundRobinPool(5).props(Props[Worker])), “router”)
Paths can be local or remote actor paths
…or from config
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akka.actor.deployment { /service/router { router = round-robin-pool resizer { lower-bound = 12 upper-bound = 15 } } }
Turn on clustering
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akka { actor { provider = "akka.cluster.ClusterActorRefProvider" ... } cluster { seed-nodes = [ “akka.tcp://ClusterSystem@127.0.0.1:2551", “akka.tcp://ClusterSystem@127.0.0.1:2552" ] auto-down = off } }
Use clustered routers
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akka.actor.deployment { /service/master { router = consistent-‐hashing-‐pool nr-‐of-‐instances = 100 ! cluster { enabled = on max-nr-of-instances-per-node = 3 allow-‐local-‐routees = on use-‐role = compute } } }
Use clustered routers
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akka.actor.deployment { /service/master { router = consistent-‐hashing-‐pool nr-‐of-‐instances = 100 ! cluster { enabled = on max-nr-of-instances-per-node = 3 allow-‐local-‐routees = on use-‐role = compute } } }
Or perhaps use an AdaptiveLoadBalancingPool
Use clustered pub-sub
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Use clustered pub-sub
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class Subscriber extends Actor { val mediator = DistributedPubSubExtension(context.system).mediator mediator ! Subscribe(“content”, self) def receive = { … } }
Use clustered pub-sub
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class Publisher extends Actor { val mediator = DistributedPubSubExtension(context.system).mediator def receive = { case in: String => mediator ! Publish("content", in.toUpperCase) } }
• Cluster Membership • Cluster Leader • Clustered Singleton • Cluster Roles • Cluster Sharding
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Other Akka Cluster features
• Supports two different models: • Command Sourcing — at least once
• Event Sourcing — at most once
• Great for implementing • durable actors • replication • CQRS etc.
• Messages persisted to Journal and replayed on restart
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Use Akka Persistence
Akka Persistence Webinar
Command Sourcing Event Sourcingwrite-ahead-log derive events from a command
same behavior during recovery as normal operation external interaction can be problematic
only state-changing behavior during recovery
persisted before validation events cannot fail
allows retroactive changes to the business logic
fixing the business logic will not affect persisted events
naming: represent intent, imperative naming: things that have completed, verbs in past tense
Akka Persistence Webinar
Life beyond Distributed Transactions: an Apostate’s Opinion
Position Paper by Pat Helland
“In general, application developers simply do not implement large scalable applications assuming distributed transactions.”
Pat Helland
http://www-‐db.cs.wisc.edu/cidr/cidr2007/papers/cidr07p15.pdf
Akka Persistence Webinar
Consistency boundary
• Aggregate Root is the Transactional Boundary • Strong consistency within an Aggregate • Eventual consistency between Aggregates
• No limit to scalability
Akka Persistence Webinar
Domain Events• Things that have completed, facts
• Immutable
• Verbs in past tense • CustomerRelocated • CargoShipped • InvoiceSent
“State transitions are an important part of our problem space and should be modeled within our domain.”
Greg Young, 2008
DEMO TIMEPersist a game of ping pong
Reactive applications enrich the user experience with low latency response.
Responsive• Real-time, engaging, rich and collaborative
• Create an open and ongoing dialog with users • More efficient workflow; inspires a feeling of connectedness • Fully Reactive enabling push instead of pull
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“The move to these technologies is already paying off. Response times are down for processor intensive code–such as image
and PDF generation–by around 75%.”
Brian Pugh, VP of Engineering, Lucid Software
Responsive
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• Keep latency consistent—under:
1. Blue sky scenarios
2. Traffic spikes
3. Failures
• The system should always be responsive
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Typesafe Reactive Platform
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Typesafe Reactive Platform
• Asynchronous and immutable programming constructs
• Composable abstractions enabling simpler concurrency and parallelism
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Typesafe Reactive Platform
• Actors are asynchronous and communicate via message passing
• Supervision and clustering in support of fault tolerance
• Asynchronous and immutable programming constructs
• Composable abstractions enabling simpler concurrency and parallelism
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Typesafe Reactive Platform
• Actors are asynchronous and communicate via message passing
• Supervision and clustering in support of fault tolerance
• Purely asynchronous and non-blocking web frameworks
• No container required, no inherent bottlenecks in session management
• Asynchronous and immutable programming constructs
• Composable abstractions enabling simpler concurrency and parallelism
Reactive is being adopted acrossa wide range of industries.
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Finance Internet/Social Media Mfg/Hardware Government Retail
Questions?
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