Target audience: Intermediate
Estimated reading time: 5'
This article describes and implements the Master-Workers design for distributed computing using Akka actors and Scala programming language.
Table of contents
Overview
Traditional multi-threaded applications rely on accessing data located in shared memory. The mechanism relies on synchronization monitors such as locks, mutexes or semaphores to avoid deadlocks and inconsistent mutable states. Those applications are difficult to debug because of race condition and incur the cost of a large number of context switches.
The Actor model addresses those issues by using immutable data structures (messages) and asynchronous (non-blocking) communication. The actor model has already been described in the previous post "Scala Share-nothing Actors".
Note: This post focuses on the simple Master-worker model using Akka framework 2.3.4 and Scala 2.11.7
Master-workers model
In this design, the "worker" actors are initialized and managed by the "master" actor which is responsible for controlling the iterative process, state, and termination condition of the algorithm. The orchestration of the distributed tasks (or steps) executing the algorithm is performed through message passing:
* Activate from master to workers to launch the execution of distributed tasks
* Complete from workers to master to notify completion of tasks and return results.
* Terminate from master to terminate the worker actors.
The first step is to defined the immutable messages.
sealed abstract class Message(val id: Int)
case class Activate(i: Int, xt: Array[Double]) extends Message(i)
case class Completed(i: Int, xt: Array[Double]) extends Message(i)
case class Start(i: Int =0) extends Message(i)
The Start message is sent to the master by the client code, (external to the master-workers communication) to launch the computation.
The following sequence diagram illustrates the management of worker' tasks by the master actor through immutable, asynchronous messages.
The following sequence diagram illustrates the management of worker' tasks by the master actor through immutable, asynchronous messages.
The next step is to define the key attributes of the master. The constructor takes 4 arguments:
* A time series xt (line 5)
* A transformation function fct (line 6)
* A data partitioner (line 7)
* A method to aggregate the results from all the worker actors aggr (line 8)
* A time series xt (line 5)
* A transformation function fct (line 6)
* A data partitioner (line 7)
* A method to aggregate the results from all the worker actors aggr (line 8)
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16 | type DblSeries = Array[Array[Double]]
type DblVector = Array[Double]
abstract class Master(
xt: DblSeries,
fct: DblSeries => DblSeries,
partitioner: Partitioner,
aggr: (List[DblSeries]) => immutable.Seq[DblVector]) extends Actor{
val workers = List.tabulate(partitioner.numPartitions)(n =>
context.actorOf(Props(new Worker(n, fct)),
name = s"${worker_ String.valueOf(n)}"))
workers.foreach( context.watch ( _ ) )
// ...
}
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The master actor creates list of worker actors, workers using the higher order method tabulate (line 10). The master registers the worker actor to be notified of their termination context.watch (line 14).
Events handler
In the implementation of the event handler receive for the master below, the Start message triggers the partitioning of the original dataset through a split function (line 3).
Upon completion of their tasks, the workers emit a Completed message to the master (line 6). The master counts the number of workers which have completed their tasks. Once all the workers have completed their tasks with the condition aggregator.size >= partitioner.numPartitions-1, the master computes the aggregated value (line 8), aggr then stop all the workers through its context workers.foreach( context.stop(_) ) (line 9).
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22 | override def receive = {
// Sent by client to master to initiate the computation
case s: Start => split
// Sent by workers on completion of their computation
case msg: Completed => {
if(aggregator.size >= partitioner.numPartitions-1) {
val aggr = aggregate.take(MAX_NUM_DATAPOINTS).toArray
workers.foreach( context.stop(_) )
}
aggregator.append(msg.xt)
}
// Sent by client to shutdown master and workers
case Terminated(sender) => {
// wait the current execution of workers completes
if( aggregator.size >= partitioner.numPartitions-1) {
context.stop(self)
context.system.shutdown
}
}
}
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The message Terminated (line 15) shuts down the master and the global context for all the actors, context.system.shutdown (lines 18 & 19).
The next step consists of defining the tasks for the worker actors. A worker actors is fully specified by its id and the data transformation fct (lines 2 & 3).
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12 | final class Worker(
id: Int,
fct: DblSeries => DblSeries) extends Actor {
override def receive = {
// Sent by master to start execution
case msg: Activate => {
val msgId = msg.id+id
val output = fct(msg.xt)
sender ! Completed(msgId, output)
}
}
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The event loop processes only one type of message, Activate, (line 7) which executes the data transformation fct (lines 8 & 9).
The last step is the implementation of the test application. Let's consider the case of the cancellation of noise on a very large dataset xt executed across multiple worker actors. The dedicated master actor of type NoiseRemover partitions the dataset using an instance of Partitioner distributed the cancellation algorithm cancelNoise to its worker (or slave) actors. The results aggregation function aggr has to be defined for this specific operation.
def cancelNoise(xt: DblSeries): DblSeries
class NoiseRemover(
xt: DblSeries,
partitioner: Partitioner,
aggr: List[DblSeries] => immutable.Seq[DblVector])
extends Master(xt, cancelNoise, partitioner, aggr)
The Akka actor context ActorSystem is initialized (line 1). The test driver implements a very simple results aggregation function aggregate passed as parameter of the noise remover master actor, controller (line 4). The reference to the controller is generated by the Akka actor factory method ActorSystem.actorOf (line 8).
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12 | val actorSystem = ActorSystem("System")
// Specifies the aggregator used in the master
def aggregate(aggr: List[DblSeries]): Seq[DblVector] =
aggr.transpose.map( _.sum).toSeq
// Create the Akka master actor
val controller = actorSystem.actorOf(
Props(new NoiseRemover(xt, partitioner, aggregate)), "Master"
)
controller ! Start(1)
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Finally the execution is started with a "fire and forget" message Start (line 12)
References
- Introducing Akka - J. Boner - Typesafe 2012
- Akka Essentials - G Manish - Packt Publishing - 2012
- Scala for Machine Learning - P. Nicolas - Packt Publishing - 2014
