Concurrency, Scalability & Fault-tolerance 2.0 with Akka Actors & STM

Concurrency, Scalability
& Fault-tolerance 2.0 with Actors & STM
by Mario Fusco
mario.fusco@gmail.com
twitter: @mariofusco

Which are the biggestchallengesin contemporary software development?

Deal with overloadedsystems

Do concurrencyeffectively

Design, develop and deployapplicationsthat scale well

The native Java concurrency model
Based on:
Threads
Semaphores
Synchronization
Locks
They are sometimes plain evil …
… and sometimes a necessary pain …
… but always the wrong default

Actors: a different model of concurrency
Implements Message-Passing Concurrency
Share NOTHING
Isolated lightweight processes
Communicates through messages
Asynchronous and non-blocking
Each actor has a mailbox (message queue)
A single thread can managemany (thousands of) actors

People are bad at parallel thinking
With actors is easier to reason about and easier to avoid:
Race conditions, Deadlocks, Starvation and Live locks

What is Akka?
An actor based concurrency framework
Written in Scala, butalsoworking in Java
Nowrealeasing 1.0 after 2 years of development (currentlyRC4 available)
Better performances and throughput and smallermemoryfootprint (650 bytes) than native scala actors
Open source (Apache2)

Untyped Actors
public class MyUntypedActorextends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceofString) {
String msg = (String)message;
System.out.println("Received message: " + msg);
}
}
}
UntypedActorRef actor = UntypedActor.actorOf(MyUntypedActor.class);
actor.start();
// --- Do some work here ---
actor.stop();

Sending message to UntypedActors
There are 3 different ways to send a message to an actor:
Fire-and-forget ( ! In Scala )
actor.sendOneWay(msg);
Uses a Future under the hood, blocking the sender Actor until eithera reply is received or the Future times out ( !! In Scala )
Object res1 = actor.sendRequestReply(msg);
Object res2 = actor.sendRequestReply(msg, 1000); // timeout
Explicitly returns a Future ( !!! In Scala )
Future future= actor.sendRequestReplyFuture(msg);
IMPORTANT: Messages can be any kind of object but have to be immutable.
Akkacan’t enforce immutability (yet) so this has to be by convention

Typed Actors
public class CounterImplextends TypedActorimplements Counter {
private int counter = 0;
public void count() {
counter++;
System.out.println(counter);
}
public intgetCountNr() {
returncounter;
}
}
Counter counter= (Counter)TypedActor
.newInstance(Counter.class, CounterImpl.class, 1000);
counter.count(); // Fire-and-forget
intcount = counter.getCountNr(); // uses Future under the hood

“Let it crash”
fault-tolerance
through
supervisor hierarchies

You can’t avoid failures: shit happens

What’swrong in trying to preventerrors?
Java and other languages and frameworks (not designed with concurrency in mind) signal faults/errors with exceptions
Throwing an exception in concurrent code will just simply blow up the thread that currently executes the code that threw it:
There is no way to find out that things went wrong, apart from inspecting the stack trace
There is nothing you can do to recover from the problem and bring back your system to a normal functioning

Fail fast & build self-repairing systems

Supervisedactors
Actors provide a clean way of getting error notification and do something about it
Supervised actors also allow you to create sets (hierarchies) of actors where you can be sure that either all are dead or none are dead
That encourages non-defensive programming: don’t try to prevent things from go wrong, because they will. Failures are a natural state in the life-cycle of your app: crash early and let someone else (that sees the whole picture), deal with it
A supervisor is responsible for starting, stopping and monitoring its child processes. It can act with 2 fault handling strategies:
One-For-One
or All-For-One

Declarative supervised actors configuration
Supervisor supervisor = new Supervisor(
new SupervisorConfig(
new AllForOneStrategy( // or OneForOneStrategy
new Class[]{Exception.class}, // Exceptions to handle
3, // maximum number of restart retries
5000), // within time in millis
new Supervise[] {
new Supervise(
UntypedActor.actorOf(MyActor1.class),
permanent()), // the actor will always be restarted
new Supervise(
UntypedActor.actorOf(MyActor2.class),
temporary()) // the actor won’t be shut down normally
}));
// You can also link and unlink actors from a supervisor
supervisor.link(actor1);
supervisor.unlink(actor1);

Remote actors
Akka provides remote actors in a completely transparent way both in regards to sending messages, error handling and supervision
Uses a very efficient and scalable I/O implementation built upon JBossNetty and Google Protocol Buffers
Can be secured with a cookie authentication mechanism
Can be bothTyped and Untyped
Twotypes of remote actors:
server-managed: run on the machine thatcreateditwhile client can lookup and obtain a proxy
client managed: makethemrunning on a different machine while the localone just hold a proxy to sendthemmessages

Server-managed
Client-managed
Typed
Bean actor =
TypedActor.newInstance(
Bean.class, BeanImpl.class,
timeout);
Actors.remote()
.start("localhost", 9999)
.registerTypedActor(
“my-service", actor
);
Bean remoteActor =
(Bean)TypedActor
.newRemoteInstance(
Bean.class, BeanImpl.class,
timeout, "192.68.23.69", 9999
);
Untyped
class MyActor
extends UntypedActor {}
Actors.remote()
.actorOf(MyActor.class,
"192.68.23.69", 9999
);
class MyActor
extends UntypedActor {}
Actors.remote()
.start("localhost", 9999)
.register(“my-service",
actorOf(MyActor.class)
);

Software Transactional Memory
An STM turns the Java heap into a transactional data set with begin/commit/rollback semantics. Very much like a regular database.
It implements the first three letters in ACID; ACI:
Atomic: all or none of the changes made during a transaction get applied
Consistent: a transaction gets a consistent of reality
Isolated: changes made by concurrent execution transactions are not visible to each other
Akka’s STM implements the concept already present in Clojure’s STM
The STM is based on Transactional References (referred to as Refs). Refs:
are memory cells, holding an (arbitrary) immutable value
implement CAS (Compare-And-Swap) semantics
are managed and enforced by the STM for coordinated changes across many Refs
are implemented using the excellent Multiverse STM (http://multiverse.codehaus.org)

Why we need an STM?
public class Container {
private int value;
private final ReadWriteLock lock = new ReentrantReadWriteLock();
public intgetValue() {
lock.readLock().lock();
try {
return value;
} finally {
lock.readLock.unlock();
}
}
public void setValue(int value) {
lock.writeLock().lock();
try {
this.value= value;
} finally {
lock.writeLock.unlock();
}
}
}
What’swrong with it?
Ad-hoc and hardlyreusablelogic
Unnecessarycomplexity
Lockslimitconcurrency and thenthroughput

How does Akka STM work?
// Create a Ref with an initial value (also possible without)
final Ref<Integer> ref = new Ref<Integer>(0);

public int counter() {
// A transaction is delimited using an Atomic anonymous inner class
return new Atomic<Integer>() {
public Integer atomically() {
intinc = ref.get() + 1; // Accessing the value of the Ref
ref.set(inc); // Changing the value of the Ref
return inc;
}
}.execute();
}
A transaction is automatically retried when it runs into some read or write conflict
In this case an (exponential) delay isused to preventfurthercontentions
Thereshouldn’t be side-effects inside the transaction to avoid to repeatthem

Transactional Datastructures
final TransactionalMap<String, User> users =
new TransactionalMap<String, User>();
// fill users map (in a transaction)
new Atomic() {
public Object atomically() {
users.put("bill", new User("bill"));
users.put("mary", new User("mary"));
users.put("john", new User("john"));
return null;
}
}.execute();
// access users map (in a transaction)
User user = new Atomic<User>() {
public User atomically() {
return users.get("bill").get();
}
}.execute();

Coordinating Actors with STM
public class Counter extends UntypedActor {
private Ref<Integer> count = new Ref(0);
private void increment() { count.set(count.get() + 1); }
public void onReceive(Object incoming) throws Exception {
if (incoming instanceof Coordinated) {
Coordinated coordinated = (Coordinated) incoming;
Object message = coordinated.getMessage();
if (message instanceof Increment) {
Increment increment = (Increment) message;
if (increment.hasFriend()) {
increment.getFriend()
.sendOneWay(coordinated.coordinate(new Increment()));
}
coordinated.atomic(new Atomically() {
public void atomically() {
increment();
}
});
}}}}

Actors + STM = Transactors
public class Counter extends UntypedTransactor {
Ref<Integer> count = new Ref<Integer>(0);
@Override public Set<SendTo> coordinate(Object message) {
Increment increment = (Increment) message;
if (increment.hasFriend())
return include(increment.getFriend(), new Increment());
}
return nobody();
}
public void atomically(Object message) {
count.set(count.get() + 1);
}
}
}

Other Akka goodies
Implements Advanced Message Queuing Protocol (AMPQ) based on the RabbitMQ
Integration with Apache Camel
PersistencemodulesupportingmanyNoSQL DB like: Cassandra, MongoDB, Riak, Redis, Terrastore, CouchDB, Voldemort, Hbase
Integration betweenAkkaSTM and JTA (Java Transaction API)
OSGisupport
Integration with Spring allowing to defineTyped Actors as Spring managed bean
Integration with Lift Web Framework and Play! Framework

Thanks a lot!
Happy Java (or Scala) hing
by Mario Fusco
mario.fusco@gmail.com
twitter: @mariofusco

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Concurrency, Scalability & Fault-tolerance 2.0 with Akka Actors & STM

by Mario Fusco

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