How to implement reliable Periodic Actor? - scala

I am using PeriodicActor taken from akka-pattern with some minor changes
trait PeriodicActor[T] extends DecoratingActor with ActorLogging {
import context.dispatcher
var messages = new ListBuffer[T]
abstract override def preStart() = {
schedule()
super.preStart()
}
protected def schedule() {
context.system.scheduler.scheduleOnce(1 seconds, self, Tick)
}
receiver {
case Tick => {
flush()
schedule()
}
}
def flush() = {
handleMessages(this.messages)
.recover {
case NonFatal(e) => log.error(e, "error in actor")
}
this.messages.clear()
}
/**
* implement to handle buffered messages.
*/
def handleMessages(messages: ListBuffer[T]): Future[Any]
def buffer(msg: T) = {
messages.append(msg)
}
}
Every 1 second a Tick is sent to self, to flush all messages received during this second.
My problem is that scheduleOnce is not reliable enough. The Tick message might not get received, which means the Ticking mechanism will stop working.
So I thought of ways to make sure this will not stop:
Maybe add if statement on the buffer method, that will make sure for example the if the list get to a certain size, it will send a Tick to flush it. The problem here, is that if the there is one lost Tick message, the list might not get flushed for a while, which is not good for me.
thought maybe to add another scheduling of Tick message every 5 minutes to make sure the mechanism will keep working.
context.system.scheduler.schedule(5 minutes, longInterval, self, Tick)
What do you think? Is there a better way?

Your problem caused by the at-most-once delivery semantics which Akka guarantees. Since you're prepares consider sending additional Ticks every 5 minutes, let's assume that you don't need exactly-once delivery of Ticks, so the occasional extra Tick is acceptable. This means that you need at-least-once delivery of Ticks.
at-least-once is achieved using some sort of ACK-RETRY, in this case you could achieve this using the ask pattern.
protected def schedule() {
context.system.scheduler.scheduleOnce(1 seconds){
sendTick()
}
}
def sendTick() {
implicit val timeout = 1 second
(self ? Tick).onComplete{
case Success(_) => schedule() // <- queue up next tick
case Failure(_) => sendTick() // <- send another tick immediately
}
}
In your receiver, you need to provide the acknowledgement:
receiver {
case Tick => {
sender ! TickAck
flush()
schedule()
}
}
More information on akka delivery guarantees: http://www.mjlivesey.co.uk/2016/02/19/akka-delivery-guarantees.html
As well as lost messages another cause of failure is the actor dying. This requires a supervision strategy. The default strategy is to restart the actor, and the postRestart() method by default calls preStart() so unless you use a different supervision strategy or override postRestart() you should get acceptable behaviour; if the actor dies it will restart and schedule another Tick.

Related

Checking message query by actor in between calculations in scala/akka

I have an actor, that when he receives one message he starts to do come calculations in a loop, and he does them for some time (like 100 times he does the same). Now I need him to react to other mesages that may come ASAP. The best way would be to add some instruction in his loop like "if there is a message in queue react and then return here" but I haven't seen such functionality.
I thought that actor could send message to himself instead of doing a loop, then such messages would be queued at the end and he would react to other ones in between, but I've heard that communication is bad (much more time consuming than calculations) and don't know if communication with self counts as such.
My question is what do you think about such solution and do you have anyother ideas how to handle communication inbetween calculations?
Time-consuming computation should not be done in the main receive method as it reduces responsiveness of the system. Put the computation in a blocking
Future or Task or other asynchronous object, and send a message to the actor when the computation completes. The actor can continue to process messages ASAP while the computation continues on a different thread.
This gets more complicated if the actor needs to modify the computation while it is running (in response to messages) but the solution depends on what the computation is and what kind of modification is needed, so it isn't really possible to give a general answer.
In general in Akka you want to limit the amount of work done "per unit" where a unit in this case:
an actor processing a message
work done in a Future/Task or a callback of the same
Overlong work units can easily limit the responsiveness of the entire system by consuming a thread. For tasks which aren't consuming CPU but are blocked waiting for I/O, those can be executed in a different thread pool, but for doing some CPU-consuming work, that doesn't really help.
So the broad approach, if you're doing a loop, is to suspend the loop's state into a message and send it to yourself. It introduces a small performance hit (the latency of constructing the message, sending it to yourself (a guaranteed-to-be local send), and destructuring it is likely going to be on the order of microseconds when the system is otherwise idle), but can improve overall system latency.
For example, imagine we have an actor which will calculate the nth fibonacci number. I'm implementing this using Akka Typed, but the broad principle applies in Classic:
object Fibonacci {
sealed trait Command
case class SumOfFirstN(n: Int, replyTo: ActorRef[Option[Long]]) extends Command
private object Internal {
case class Iterate(i: Int, a: Int, b: Int) extends Command
val initialIterate = Iterate(1, 0, 1)
}
case class State(waiting: SortedMap[Int, Set[ActorRef[Option[Long]]]]) {
def behavior: Behavior[Command] =
Behaviors.receive { (context, msg) =>
msg match {
case SumOfFirstN(n, replyTo) =>
if (n < 1) {
replyTo ! None
Behaviors.same
} else {
if (waiting.isEmpty) {
context.self ! Internal.initialIterate
}
val nextWaiting =
waiting.updated(n, waiting.get(n).fold(Set(replyTo))(_.incl(replyTo))
copy(waiting = nextWaiting).behavior
}
case Internal.Iterate(i, a, b) =>
// the ith fibonacci number is b, the (i-1)th is a
if (waiting.rangeFrom(i).isEmpty) {
// Nobody waiting for this run to complete
if (waiting.nonEmpty) {
context.self ! Internal.initialIterate
}
Behaviors.same
} else {
var nextWaiting = waiting
var nextA = a
var nextB = b
(1 to 10).foreach { x =>
val next = nextA + nextB
nextWaiting.get(x + i).foreach { waiters =>
waiters.foreach(_ ! Some(next))
}
nextWaiting = nextWaiting.removed(x + i)
nextA = nextB
nextB = next
}
context.self ! Internal.Iterate(i + 10, nextA, nextB)
copy(waiting = nextWaiting)
}
}
}
}
}
Note that multiple requests (if sufficiently temporally close) for the same number will only be computed once, and temporally close requests for intermediate results will result in no extra computation.
An option is to delegate the task, using for eg:Future, and use a separate ExecutionContext with a fixed-pool-size (configurable in application.conf) equal to the number of CPUs (or cores) so that the computations are done efficiently using the available cores. As mentioned by #Tim, you could notify the main actor once the computation is complete.
Another option is to make another actor behind a router do the computation while restricting the number of routees to the number of CPUs.
A simplistic sample:
object DelegatingSystem extends App {
val sys = ActorSystem("DelegatingSystem")
case class TimeConsuming(i: Int)
case object Other
class Worker extends Actor with ActorLogging {
override def receive: Receive = {
case message =>
Thread.sleep(1000)
log.info(s"$self computed long $message")
}
}
class Delegator extends Actor with ActorLogging {
//Set the number of routees to be equal to #of cpus
val router: ActorRef = context.actorOf(RoundRobinPool(2).props(Props[Worker]))
override def receive: Receive = {
case message:TimeConsuming => router ! message
case _ =>
log.info("process other messages")
}
}
val delegator = sys.actorOf(Props[Delegator])
delegator ! TimeConsuming(1)
delegator ! Other
delegator ! TimeConsuming(2)
delegator ! Other
delegator ! TimeConsuming(3)
delegator ! Other
delegator ! TimeConsuming(4)
}

How do I call context become outside of a Future from Ask messages?

I have a parent akka actor named buildingCoordinator which creates childs name elevator_X. For now I am creating only one elevator. The buildingCoordinator sends a sequence of messages and wait for responses in order to move an elevator. The sequence is this: sends ? RequestElevatorState -> receive ElevatorState -> sends ? MoveRequest -> receives MoveRequestSuccess -> changes the state. As you can see I am using the ask pattern. After the movement is successes the buildingCoordinator changes its state using context.become.
The problem that I am running is that the elevator is receiving MoveRequest(1,4) for the same floor twice, sometimes three times. I do remove the floor when I call context.become. However I remove inside the last map. I think it is because I am using context.become inside a future and I should use it outside. But I am having trouble implementing it.
case class BuildingCoordinator(actorName: String,
numberOfFloors: Int,
numberOfElevators: Int,
elevatorControlSystem: ElevatorControlSystem)
extends Actor with ActorLogging {
import context.dispatcher
implicit val timeout = Timeout(4 seconds)
val elevators = createElevators(numberOfElevators)
override def receive: Receive = operational(Map[Int, Queue[Int]](), Map[Int, Queue[Int]]())
def operational(stopsRequests: Map[Int, Queue[Int]], pickUpRequests: Map[Int, Queue[Int]]): Receive = {
case msg#MoveElevator(elevatorId) =>
println(s"[BuildingCoordinator] received $msg")
val elevatorActor: ActorSelection = context.actorSelection(s"/user/$actorName/elevator_$elevatorId")
val newState = (elevatorActor ? RequestElevatorState(elevatorId))
.mapTo[ElevatorState]
.flatMap { state =>
val nextStop = elevatorControlSystem.findNextStop(stopsRequests.get(elevatorId).get, state.currentFloor, state.direction)
elevatorActor ? MoveRequest(elevatorId, nextStop)
}
.mapTo[MoveRequestSuccess]
.flatMap(moveRequestSuccess => elevatorActor ? MakeMove(elevatorId, moveRequestSuccess.targetFloor))
.mapTo[MakeMoveSuccess]
.map { makeMoveSuccess =>
println(s"[BuildingCoordinator] Elevator ${makeMoveSuccess.elevatorId} arrived at floor [${makeMoveSuccess.floor}]")
// removeStopRequest
val stopsRequestsElevator = stopsRequests.get(elevatorId).getOrElse(Queue[Int]())
val newStopsRequestsElevator = stopsRequestsElevator.filterNot(_ == makeMoveSuccess.floor)
val newStopsRequests = stopsRequests + (elevatorId -> newStopsRequestsElevator)
val pickUpRequestsElevator = pickUpRequests.get(elevatorId).getOrElse(Queue[Int]())
val newPickUpRequestsElevator = {
if (pickUpRequestsElevator.contains(makeMoveSuccess.floor)) {
pickUpRequestsElevator.filterNot(_ == makeMoveSuccess.floor)
} else {
pickUpRequestsElevator
}
}
val newPickUpRequests = pickUpRequests + (elevatorId -> newPickUpRequestsElevator)
// I THINK I SHOULD NOT CALL context.become HERE
// context.become(operational(newStopsRequests, newPickUpRequests))
val dropOffFloor = BuildingUtil.generateRandomFloor(numberOfFloors, makeMoveSuccess.floor, makeMoveSuccess.direction)
context.self ! DropOffRequest(makeMoveSuccess.elevatorId, dropOffFloor)
(newStopsRequests, newPickUpRequests)
}
// I MUST CALL context.become HERE, BUT I DONT KNOW HOW
// context.become(operational(newState.flatMap(state => (state._1, state._2))))
}
Other thing that might be nasty here is this big chain of map and flatMap. This was my way to implement, however I think it might exist one way better.
You can't and you should not call context.become or anyhow change actor state outside Receive method and outside Receive method invoke thread (which is Akka distpatcher thread), like in your example. Eg:
def receive: Receive = {
// This is a bug, because context is not and is not supposed to be thread safe.
case message: Message => Future(context.become(anotherReceive))
}
What you should do - send message to self after async operation finished and change the state receive after. If in a mean time you don't want to handle incoming messages - you can stash them. See for more details: https://doc.akka.io/docs/akka/current/typed/stash.html
High level example, technical details omitted:
case OperationFinished(calculations: Map[Any, Any])
class AsyncActor extends Actor with Stash {
def operation: Future[Map[Any, Any]] = ...//some implementation of heavy async operation
def receiveStartAsync(calculations: Map[Any, Any]): Receive = {
case StartAsyncOperation =>
//Start async operation and inform yourself that it is finished
operation.map(OperationFinished.apply) pipeTo self
context.become(receiveWaitAsyncOperation)
}
def receiveWaitAsyncOperation: Receive = {
case OperationFinished =>
unstashAll()
context.become(receiveStartAsync)
case _ => stash()
}
}
I like your response #Ivan Kurchenko.
But, according to: Akka Stash docs
When unstashing the buffered messages by calling unstashAll the messages will be processed sequentially in the order they were added and all are processed unless an exception is thrown. The actor is unresponsive to other new messages until unstashAll is completed. That is another reason for keeping the number of stashed messages low. Actors that hog the message processing thread for too long can result in starvation of other actors.
Meaning that under load, for example, the unstashAll operation will cause all other Actors to be in starvation.
According to the same doc:
That can be mitigated by using the StashBuffer.unstash with numberOfMessages parameter and then send a message to context.self before continuing unstashing more. That means that other new messages may arrive in-between and those must be stashed to keep the original order of messages. It becomes more complicated, so better keep the number of stashed messages low.
Bottom line: you should keep the stashed message count low. It might be not suitable for load operation.

Can actors read messages under a certain condition?

I have this situation:
ActorA sends ActorB start/stop messages every 30-40 seconds
ActorA sends ActorB strings to print (always)
ActorB must print the strings he receive, but only if ActorA sent just a start message
Now i wonder if i can do the following things:
Can ActorB read messages only under a certain condition (if a boolean is set as true) without losing the messages he receives while that boolean is set as false?
Can ActorB read a start/stop message from ActorA before the other string messages? I'd like to have this situation: ActorA sends a start message to ActorB, ActorB start printing the strings he received before the start messages (and that is still receiving) and then stop as soon as it receives a stop messages?
I don't know if I explained it well.
EDIT: Thank you, the answers are great, but I still have some doubts.
Does the become mantain the order of the messages? I mean, if i have "Start-M1-M2-Stop-M3-M4-M5-Start-M6-M7-Stop", will the printing order be "M1-M2" and then "M3-M4-M5-M6-M7" or could M6 be read before M3, M4 and M5 (if M6 is received just after the become)?
Can I give a higher priority to start/stop messages? If ActorB receives "M1-M2-M3", and then it receives a stop message while it is printing "M1", i want that ActorB saves again M2 and M3.
You can exactly solve your problem with the Stash trait and the become/unbecome functionality of Akka. The idea is the following:
When you receive a Stop message then you switch to a behaviour where you stash all messages which are not Start. When you receive a Start message, then you switch to a behaviour where you print all received messages and additionally you unstash all messages which have arrived in the meantime.
case object Start
case object Stop
case object TriggerStateChange
case object SendMessage
class ActorB extends Actor with Stash {
override def receive: Receive = {
case Start =>
context.become(printingBehavior, false)
unstashAll()
case x => stash()
}
def printingBehavior: Receive = {
case msg: String => println(msg)
case Stop => context.unbecome()
}
}
class ActorA(val actorB: ActorRef) extends Actor {
var counter = 0
var started = false
override def preStart: Unit = {
import context.dispatcher
this.context.system.scheduler.schedule(0 seconds, 5 seconds, self, TriggerStateChange)
this.context.system.scheduler.schedule(0 seconds, 1 seconds, self, SendMessage)
}
override def receive: Actor.Receive = {
case SendMessage =>
actorB ! "Message: " + counter
counter += 1
case TriggerStateChange =>
actorB ! (if (started) {
started = false
Stop
} else {
started = true
Start
})
}
}
object Akka {
def main(args: Array[String]) = {
val system = ActorSystem.create("TestActorSystem")
val actorB = system.actorOf(Props(classOf[ActorB]), "ActorB")
val actorA = system.actorOf(Props(classOf[ActorA], actorB), "ActorA")
system.awaitTermination()
}
}
Check the Become/Unbecome functionality. It lets you change the behavior of the actor.
If I understood correctly you want your ActorB to have two different states. In the first state it should cache the messages it receives. In the second state, it should print all the cached messages and start printing all the new ones.
Something like this:
case class Start
case class Stop
case class Message(val: String)
class ActorB extends Actor {
var cache = Queue()
def initial: Receive = {
case Message(msg) => cache += msg
case Start =>
for (m <- cache) self ! Message(m)
context.become(printing)
}
def printing: Receive = {
case Message(msg) => println(msg)
case Stop => context.become(initial) //or stop the actor
}
def receive = initial
}
Have Actor B alternate between two states (two different behaviours). In the initial 'pending' state, it waits for a 'start' message, while stashing any other messages.
On receipt of a 'start' message, unstash all the stored messages and become 'active', waiting on a 'stop' message and writing out the other messages received (which will include the unstashed ones). On receiveing a 'stop' message, revert to the 'pending' behaviour.
Some of my thoughts
Yes if the boolean flag is got from some system resource like db or a config file, but I don't think it should be dependent on any external message, given that the actor receive messages from multiple other actors. If ActorB is only used by ActorA, the two can be merged to one
Similar as 1, how to handle the messages from multiple actors? If there is only one actorA, the two actors can be merged. If there are multiple, the flag can be set in database, actorA change the flag in db to "Start" or "Stop". and Actor B will print or not based on the flag.
An actor should be doing something very independently on other actor's state. The start and stop is actually some state of ActorA instead of ActorB
You already have a lot of good answers, but somehow I feel compelled to offer something more brief, as what you need is not necessarily a state machine:
class ActorB extends Actor {
def receive: Receive = caching(Nil)
def caching(cached: List[String]): Receive = {
case msg: String =>
context.become(caching(msg :: cached))
case Start =>
cached.reverse.foreach(println)
context.become(caching(Nil))
}
}

Handling Faults in Akka actors

I've a very simple example where I've an Actor (SimpleActor) that perform a periodic task by sending a message to itself. The message is scheduled in the constructor for the actor. In the normal case (i.e., without faults) everything works fine.
But what if the Actor has to deal with faults. I've another Actor (SimpleActorWithFault). This actor could have faults. In this case, I'm generating one myself by throwing an exception. When a fault happens (i.e., SimpleActorWithFault throws an exception) it is automatically restarted. However, this restart messes up the scheduler inside the Actor which no longer functions as excepted. And if the faults happens rapidly enough it generates more unexpected behavior.
My question is what's the preferred way to dealing with faults in such cases? I know I can use Try blocks to handle exceptions. But what if I'm extending another actor where I cannot put a Try in the super class or some case when I'm an excepted fault happens in the actor.
import akka.actor.{Props, ActorLogging}
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
import akka.actor.Actor
case object MessageA
case object MessageToSelf
class SimpleActor extends Actor with ActorLogging {
//schedule a message to self every second
context.system.scheduler.schedule(0 seconds, 1 seconds, self, MessageToSelf)
//keeps track of some internal state
var count: Int = 0
def receive: Receive = {
case MessageA => {
log.info("[SimpleActor] Got MessageA at %d".format(count))
}
case MessageToSelf => {
//update state and tell the world about its current state
count = count + 1
log.info("[SimpleActor] Got scheduled message at %d".format(count))
}
}
}
class SimpleActorWithFault extends Actor with ActorLogging {
//schedule a message to self every second
context.system.scheduler.schedule(0 seconds, 1 seconds, self, MessageToSelf)
var count: Int = 0
def receive: Receive = {
case MessageA => {
log.info("[SimpleActorWithFault] Got MessageA at %d".format(count))
}
case MessageToSelf => {
count = count + 1
log.info("[SimpleActorWithFault] Got scheduled message at %d".format(count))
//at some point generate a fault
if (count > 5) {
log.info("[SimpleActorWithFault] Going to throw an exception now %d".format(count))
throw new Exception("Excepttttttiooooooon")
}
}
}
}
object MainApp extends App {
implicit val akkaSystem = akka.actor.ActorSystem()
//Run the Actor without any faults or exceptions
akkaSystem.actorOf(Props(classOf[SimpleActor]))
//comment the above line and uncomment the following to run the actor with faults
//akkaSystem.actorOf(Props(classOf[SimpleActorWithFault]))
}
The correct way is to push down the risky behavior into it's own actor. This pattern is called the Error Kernel pattern (see Akka Concurrency, Section 8.5):
This pattern describes a very common-sense approach to supervision
that differentiates actors from one another based on any volatile
state that they may hold.
In a nutshell, it means that actors whose state is precious should not
be allowed to fail or restart. Any actor that holds precious data is
protected such that any risky operations are relegated to a slave
actor who, if restarted, only causes good things to happen.
The error kernel pattern implies pushing levels of risk further down
the tree.
See also another tutorial here.
So in your case it would be something like this:
SimpleActor
|- ActorWithFault
Here SimpleActor acts as a supervisor for ActorWithFault. The default supervising strategy of any actor is to restart a child on Exception and escalate on anything else:
http://doc.akka.io/docs/akka/snapshot/scala/fault-tolerance.html
Escalating means that the actor itself may get restarted. Since you really don't want to restart SimpleActor you could make it always restart the ActorWithFault and never escalate by overriding the supervisor strategy:
class SimpleActor {
override def preStart(){
// our faulty actor --- we will supervise it from now on
context.actorOf(Props[ActorWithFault], "FaultyActor")
...
override val supervisorStrategy = OneForOneStrategy () {
case _: ActorKilledException => Escalate
case _: ActorInitializationException => Escalate
case _ => Restart // keep restarting faulty actor
}
}
To avoid messing up the scheduler:
class SimpleActor extends Actor with ActorLogging {
private var cancellable: Option[Cancellable] = None
override def preStart() = {
//schedule a message to self every second
cancellable = Option(context.system.scheduler.schedule(0 seconds, 1 seconds, self, MessageToSelf))
}
override def postStop() = {
cancellable.foreach(_.cancel())
cancellable = None
}
...
To correctly handle exceptions (akka.actor.Status.Failure is for correct answer to an ask in case of Ask pattern usage by sender):
...
def receive: Receive = {
case MessageA => {
try {
log.info("[SimpleActor] Got MessageA at %d".format(count))
} catch {
case e: Exception =>
sender ! akka.actor.Status.Failure(e)
log.error(e.getMessage, e)
}
}
...

Is it possible to use 'react' to wait for a number of child actors to complete, then continue afterwards?

I'm getting all in a twist trying to get this to work. New to scala and to actors so may inadvertently be making bad design decisions - please tell me if so.
The setup is this:
I have a controlling actor which contains a number of worker actors. Each worker represents a calculation that for a given input will spit out 1..n outputs. The controller has to set off each worker, collect the returned outputs, then carry on and do a bunch more stuff once this is complete. This is how I approached it using receive in the controller actor:
class WorkerActor extends Actor {
def act() {
loop {
react {
case DoJob =>
for (1 to n) sender ! Result
sender ! Done
}
}
}
}
The worker actor is simple enough - it spits out results until it's done, when it sends back a Done message.
class ControllerActor(val workers: List[WorkerActor]) extends Actor {
def act() {
workers.foreach(w => w ! DoJob)
receiveResults(workers.size)
//do a bunch of other stuff
}
def receiveResults(count: Int) {
if (count == 0) return
receive {
case Result =>
// do something with this result (that updates own mutable state)
receiveResults(count)
case Done
receiveResults(count - 1)
}
}
}
The controller actor kicks off each of the workers, then recursively calls receive until it has received a Done message for each of the workers.
This works, but I need to create lots of the controller actors, so receive is too heavyweight - I need to replace it with react.
However, when I use react, the behind-the-scenes exception kicks in once the final Done message is processed, and the controller actor's act method is short-circuited, so none of the "//do a bunch of other stuff" that comes after happens.
I can make something happen after the final Done message by using andThen { } - but I actually need to do several sets of calculations in this manner so would end up with a ridiculously nested structure of andThen { andThen { andThen } }s.
I also want to hide away this complexity in a method, which would then be moved into a separate trait, such that a controller actor with a number of lists of worker actors can just be something like this:
class ControllerActor extends Actor with CalculatingTrait {
//CalculatingTrait has performCalculations method
val listOne: List[WorkerActor]
val ListTwo: List[WorkerActor]
def act {
performCalculations(listOne)
performCalculations(listTwo)
}
}
So is there any way to stop the short-circuiting of the act method in the performCalculations method? Is there a better design approach I could be taking?
You can avoid react/receive entirely by using Akka actor's. Here's what you implementation could look like:
import akka.actor._
class WorkerActor extends Actor {
def receive = {
case DoJob =>
for (_ <- 1 to n) sender ! Result
sender ! Done
}
}
class ControllerActor(workers: List[ActorRef]) extends Actor {
private[this] var countdown = workers.size
override def preStart() {
workers.foreach(_ ! DoJob)
}
def receive = {
case Result =>
// do something with this result
case Done =>
countdown -= 1
if (countdown == 0) {
// do a bunch of other stuff
// It looks like your controllers die when the workers
// are done, so I'll do the same.
self ! PoisonPill
}
}
}
Here's how I might approach it (in way that seems to be more comments and boilerplate than actual content):
class WorkerController(val workerCriteria: List[WorkerCriteria]) {
// The actors that only _I_ interact with are probably no one else's business
// Your call, though
val workers = generateWorkers(workerCriteria)
// No need for an `act` method--no need for this to even be an actor
/* Will send `DoJob` to each actor, expecting a reply from each.
* Could also use the `!!` operator (instead of `!?`) if you wanted
* them to return futures (so WorkerController could continue doing other
* things while the results compute). The futures could then be evaluated
* with `results map (_())`, which will _then_ halt execution to wait for each
* future that isn't already computed (if any).
*/
val results = workers map (_ !? DoJob)
//do a bunch of other stuff with your results
def generateWorkers(criteria: List[WorkerCriteria]) = // Create some workers!
}
class Worker extends Actor {
def act() {
loop {
react {
case DoJob =>
// Will generate a result and send it back to the caller
reply(generateResult)
}
}
}
def generateResult = // Result?
}
EDIT: Have just been reading about Akka actors and spotted that they "guarantee message order on a per sender basis". So I updated my example such that, if the controller needed to later ask the receiver for the computed value and needed to be sure it was all complete, it could do so with a message order guarantee on only a per sender basis (the example is still scala actors, not akka).
It finally hit me, with a bit of help from #Destin's answer, that I could make it a lot simpler by separating out the part of the controller responsible for kicking off the workers from the part responsible for accepting and using the results. Single responsibility principle I suppose... Here's what I did (separating out the original controlling actor into a controlling class and a 'receiver' actor):
case class DoJob(receiever: Actor)
case object Result
case object JobComplete
case object Acknowledged
case object Done
class Worker extends Actor {
def act {
loop {
react {
case DoJob(receiver) =>
receiver ! Result
receiver ! Result
receiver !? JobComplete match {
case Acknowledged =>
sender ! Done
}
}
}
}
}
class Receiver extends Actor {
def act {
loop {
react {
case Result => println("Got result!")
case JobComplete => sender ! Acknowledged
}
}
}
}
class Controller {
val receiver = new Receiver
val workers = List(new Worker, new Worker, new Worker)
receiver.start()
workers.foreach(_.start())
workers.map(_ !! DoJob(receiver)).map(_())
println("All the jobs have been done")
}