What is the best practice when defining an actor?
Actor state: is it better to define a "var" with a collection like in the code below or is it better to define a "val" with mutable collection ? should we define it as private ?
should we define methods of Actor as private ?
class FooActor(out:ActorRef)extends Actor {
private var words:List[String] = Nil
override def receive: Receive = ???
def foo()=???
}
On the first point, generally, I would go with neither. Instead, set the receive method to a method taking the collection as a parameter, and update the actor's state when the collection changes using context.become(...). Eg:
class FooActor(out:ActorRef)extends Actor {
override def receive: Receive = active(Nil)
def active(words:List[String]): Receive = Receive {
case word_to_add: String => context.become(active(word_to_add :: words))
case ...
}
private def foo()=???
}
On the second point, any helper methods are probably only for the actor's own use, so make them private.
To the first point it really depends on how large the collection of items is going to be that you're mutating. Are you going to be adding 100k items to a Map over the course of 100k messages? If this is the case perhaps you should be using a mutable collection so as to avoid the overhead of copying the entire collection to add each item. Make a smart decision based on the use case.
Here's a reference to the performance of mutable vs. immutable collections: http://www.scala-lang.org/docu/files/collections-api/collections.html
To the second point the visibility of the methods doesn't matter in terms of the interface with the Actor. The only way that you should be interacting with an Actor is through asking and telling messages so the visibility of any member methods is of little consequence outside of inferring purpose to the reader.
Related
I have the following code that set the Atomic variable (both java.util.concurrent.atomic and monix.execution.atomic behaves the same:
class Foo {
val s = AtomicAny(null: String)
def foo() = {
println("called")
/* Side Effects */
"foo"
}
def get(): String = {
s.compareAndSet(null, foo())
s.get
}
}
val f = new Foo
f.get //Foo.s set from null to foo, print called
f.get //Foo.s not updated, but still print called
The second time it compareAndSet, it did not update the value, but still foo is called. This is causing problem because foo is having side effects (in my real code, it creates an Akka actor and give me error because it tries to create duplicate actors).
How can I make sure the second parameter is not evaluated unless it is actually used? (Preferably not using synchronized)
I need to pass implicit parameter to foo so lazy val would not work. E.g.
lazy val s = get() //Error cannot provide implicit parameter
def foo()(implicit context: Context) = {
println("called")
/* Side Effects */
"foo"
}
def get()(implicit context: Context): String = {
s.compareAndSet(null, foo())
s.get
}
Updated answer
The quick answer is to put this code inside an actor and then you don't have to worry about synchronisation.
If you are using Akka Actors you should never need to do your own thread synchronisation using low-level primitives. The whole point of the actor model is to limit the interaction between threads to just passing asynchronous messages. This provides all the thread synchronisation that you need and guarantees that an actor processes a single message at a time in a single-threaded manner.
You should definitely not have a function that is accessed simultaneously by multiple threads that creates a singleton actor. Just create the actor when you have the information you need and pass the ActorRef to any other actors that need it using dependency injection or a message. Or create the actor at the start and initialise it when the first message arrives (using context.become to manage the actor state).
Original answer
The simplest solution is just to use a lazy val to hold your instance of foo:
class Foo {
lazy val foo = {
println("called")
/* Side Effects */
"foo"
}
}
This will create foo the first time it is used and after that will just return the same value.
If this is not possible for some reason, use an AtomicInteger initialised to 0 and then call incrementAndGet. If this returns 1 then it is the first pass through this code and you can call foo.
Explanation:
Atomic operations such as compareAndSet require support from the CPU instruction set, and modern processors have single atomic instructions for such operations. In some cases (e.g. cache line is held exclusively by this processor) the operation can be very fast. Other cases (e.g. cache line also in cache of another processor) the operation can be significantly slower and can impact other threads.
The result is that the CPU must be holding the new value before the atomic instruction is executed. So the value must be computed before it is known whether it is needed or not.
This is a "real life" OO design question. I am working with Scala, and interested in specific Scala solutions, but I'm definitely open to hear generic thoughts.
I am implementing a branch-and-bound combinatorial optimization program. The algorithm itself is pretty easy to implement. For each different problem we just need to implement a class that contains information about what are the allowed neighbor states for the search, how to calculate the cost, and then potentially what is the lower bound, etc...
I also want to be able to experiment with different data structures. For instance, one way to store a logic formula is using a simple list of lists of integers. This represents a set of clauses, each integer a literal. We can have a much better performance though if we do something like a "two-literal watch list", and store some extra information about the formula in general.
That all would mean something like this
object BnBSolver[S<:BnBState]{
def solve(states: Seq[S], best_state:Option[S]): Option[S] = if (states.isEmpty) best_state else
val next_state = states.head
/* compare to best state, etc... */
val new_states = new_branches ++ states.tail
solve(new_states, new_best_state)
}
class BnBState[F<:Formula](clauses:F, assigned_variables) {
def cost: Int
def branches: Seq[BnBState] = {
val ll = clauses.pick_variable
List(
BnBState(clauses.assign(ll), ll :: assigned_variables),
BnBState(clauses.assign(-ll), -ll :: assigned_variables)
)
}
}
case class Formula[F<:Formula[F]](clauses:List[List[Int]]) {
def assign(ll: Int) :F =
Formula(clauses.filterNot(_ contains ll)
.map(_.filterNot(_==-ll))))
}
Hopefully this is not too crazy, wrong or confusing. The whole issue here is that this assign method from a formula would usually take just the current literal that is going to be assigned. In the case of two-literal watch lists, though, you are doing some lazy thing that requires you to know later what literals have been previously assigned.
One way to fix this is you just keep this list of previously assigned literals in the data structure, maybe as a private thing. Make it a self-standing lazy data structure. But this list of the previous assignments is actually something that may be naturally available by whoever is using the Formula class. So it makes sense to allow whoever is using it to just provide the list every time you assign, if necessary.
The problem here is that we cannot now have an abstract Formula class that just declares a assign(ll:Int):Formula. In the normal case this is OK, but if this is a two-literal watch list Formula, it is actually an assign(literal: Int, previous_assignments: Seq[Int]).
From the point of view of the classes using it, it is kind of OK. But then how do we write generic code that can take all these different versions of Formula? Because of the drastic signature change, it cannot simply be an abstract method. We could maybe force the user to always provide the full assigned variables, but then this is a kind of a lie too. What to do?
The idea is the watch list class just becomes a kind of regular assign(Int) class if I write down some kind of adapter method that knows where to take the previous assignments from... I am thinking maybe with implicit we can cook something up.
I'll try to make my answer a bit general, since I'm not convinced I'm completely following what you are trying to do. Anyway...
Generally, the first thought should be to accept a common super-class as a parameter. Obviously that won't work with Int and Seq[Int].
You could just have two methods; have one call the other. For instance just wrap an Int into a Seq[Int] with one element and pass that to the other method.
You can also wrap the parameter in some custom class, e.g.
class Assignment {
...
}
def int2Assignment(n: Int): Assignment = ...
def seq2Assignment(s: Seq[Int]): Assignment = ...
case class Formula[F<:Formula[F]](clauses:List[List[Int]]) {
def assign(ll: Assignment) :F = ...
}
And of course you would have the option to make those conversion methods implicit so that callers just have to import them, not call them explicitly.
Lastly, you could do this with a typeclass:
trait Assigner[A] {
...
}
implicit val intAssigner = new Assigner[Int] {
...
}
implicit val seqAssigner = new Assigner[Seq[Int]] {
...
}
case class Formula[F<:Formula[F]](clauses:List[List[Int]]) {
def assign[A : Assigner](ll: A) :F = ...
}
You could also make that type parameter at the class level:
case class Formula[A:Assigner,F<:Formula[A,F]](clauses:List[List[Int]]) {
def assign(ll: A) :F = ...
}
Which one of these paths is best is up to preference and how it might fit in with the rest of the code.
I have a project where I'm implementing a vending machine. I have an object VendingMachine, which has another object inside it called CoinOp. I want the CoinOp object to accept coins of various values. The VendingMachine can ask the CoinOp to tell it how much money the user has entered. For that to really work, the CoinOp has to be able to change the amount of money it has inside it.
VendingMachine looks like this:
class VendingMachine {
val coinOp = new CoinOp()
}
I want to do this in a functional way, which immediately removes this implementation of CoinOp as an option:
class CoinOp {
var money = 0.0f
def addCoins(amount: Float) = money += amount
}
Instead I need to do something like this:
case class CoinOp(money: Float) {
def addCoins(amount: Float): CoinOp = CoinOp(money + amount)
}
I get that I can use scalaz to do something like this. The implementation in scalaz isn't really the focus of this question.
My question is this: Given that I can functionally update the amount of money the CoinOp has, how do I show VendingMachine that has changed?
Creating the new CoinOp object in addCoins is great and all, but it is a new object, and it doesn't mutate the object to which VendingMachine has a reference. Which is good persistence, all part of functional programming. But, I need the VendingMachine to use the object that addCoins() creates instead of the one that is in the class definition. How do I do that?
The only thing I can think of is to make the coinOp object in VendingMachine a var, which I can then set in response to calls to CoinOp#addCoins. But that doesn't seem like a good solution to me.
Could someone please explain how something like would be set up?
If you want to do functional programming you stick as much as possible to methods without side effect and immutable structures, like you did with your
case class CoinOp(money: Float) {
def addCoins(amount: Float): CoinOp = CoinOp(money + amount)
def addCoins(amount: CoinOp): CoinOp = CoinOp(money + amount.money)
}
So that, as you said, when calling addCoins method you will get new CoinOp instance returned.
But the problem here is your task. Having vending machine implies that you have some state that you need to maintain. And state is, unfortunately, var.
So if you don't need thread safety, you simply create private variable of type CoinOp and when adding funds - you assign another instance of CoinOp to this var. This way you will have your CoinOp class immutable, but VendingMachine will have a state and will be mutable.
class VendingMachine{
var funds: CoinOp = CoinOp(0)
def addFunds(amount: CoinOp): CoinOp = {
funds = funds.addCoins(amount.money)
// or rather use addCoins(CoinOP):
// funds = funds.addCoins(amount)
}
}
If you want to have a synchronised access to your funds state - Scala way is Akka Actor, keeping your state inside itself
For an immutable flavour, Iterator does the job.
val x = Iterator.fill(100000)(someFn)
Now I want to implement a mutable version of Iterator, with three guarantees:
thread-safe on all transformations(fold, foldLeft, ..) and append
lazy evaluated
traversable only once! Once used, an object from this Iterator should be destroyed.
Is there an existing implementation to give me these guarantees? Any library or framework example would be great.
Update
To illustrate the desired behaviour.
class SomeThing {}
class Test(val list: Iterator[SomeThing]) {
def add(thing: SomeThing): Test = {
new Test(list ++ Iterator(thing))
}
}
(new Test()).add(new SomeThing).add(new SomeThing);
In this example, SomeThing is an expensive construct, it needs to be lazy.
Re-iterating over list is never required, Iterator is a good fit.
This is supposed to asynchronously and lazily sequence 10 million SomeThing instances without depleting the executor(a cached thread pool executor) or running out of memory.
You don't need a mutable Iterator for this, just daisy-chain the immutable form:
class SomeThing {}
case class Test(val list: Iterator[SomeThing]) {
def add(thing: => SomeThing) = Test(list ++ Iterator(thing))
}
(new Test()).add(new SomeThing).add(new SomeThing)
Although you don't really need the extra boilerplate of Test here:
Iterator(new SomeThing) ++ Iterator(new SomeThing)
Note that Iterator.++ takes a by-name param, so the ++ operation is already lazy.
You might also want to try this, to avoid building intermediate Iterators:
Iterator.continually(new SomeThing) take 2
UPDATE
If you don't know the size in advance, then I'll often use a tactic like this:
def mkSomething = if(cond) Some(new Something) else None
Iterator.continually(mkSomething) takeWhile (_.isDefined) map { _.get }
The trick is to have your generator function wrap its output in an Option, which then gives you a way to flag that the iteration is finished by returning None
Of course... If you're really pushing out the boat, you can even use the dreaded null:
def mkSomething = if(cond) { new Something } else null
Iterator.continually(mkSomething) takeWhile (_ != null)
Seems like you need to hide the fact that the iterator is mutable but at the same time allow it to grow mutably. What I'm going to propose is the same sort of trick I've used to speed up ::: in the past:
abstract class AppendableIterator[A] extends Iterator[A]{
protected var inner: Iterator[A]
def hasNext = inner.hasNext
def next() = inner next ()
def append(that: Iterator[A]) = synchronized{
inner = new JoinedIterator(inner, that)
}
}
//You might need to add some more things, this is a skeleton
class JoinedIterator[A](first: Iterator[A], second: Iterator[A]) extends Iterator[A]{
def hasNext = first.hasNext || second.hasNext
def next() = if(first.hasNext) first next () else if(second.hasNext) second next () else Iterator.next()
}
So what you're really doing is leaving the Iterator at whatever place in its iteration you might have it while still preserving the thread safety of the append by "joining" another Iterator in non-destructively. You avoid the need to recompute the two together because you never actually force them through a CanBuildFrom.
This is also a generalization of just adding one item. You can always wrap some A in an Iterator[A] of one element if you so choose.
Have you looked at the mutable.ParIterable in the collection.parallel package?
To access an iterator over elements you can do something like
val x = ParIterable.fill(100000)(someFn).iterator
From the docs:
Parallel operations are implemented with divide and conquer style algorithms that parallelize well. The basic idea is to split the collection into smaller parts until they are small enough to be operated on sequentially.
...
The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.
val and var in scala, the concept is understandable enough, I think.
I wanted to do something like this (java like):
trait PersonInfo {
var name: Option[String] = None
var address: Option[String] = None
// plus another 30 var, for example
}
case class Person() extends PersonInfo
object TestObject {
def main(args: Array[String]): Unit = {
val p = new Person()
p.name = Some("someName")
p.address = Some("someAddress")
}
}
so I can change the name, address, etc...
This works well enough, but the thing is, in my program I end up with everything as vars.
As I understand val are "preferred" in scala. How can val work in this
type of example without having to rewrite all 30+ arguments every time one of them is changed?
That is, I could have
trait PersonInfo {
val name: Option[String]
val address: Option[String]
// plus another 30 val, for example
}
case class Person(name: Option[String]=None, address: Option[String]=None, ...plus another 30.. ) extends PersonInfo
object TestObject {
def main(args: Array[String]): Unit = {
val p = new Person("someName", "someAddress", .....)
// and if I want to change one thing, the address for example
val p2 = new Person("someName", "someOtherAddress", .....)
}
}
Is this the "normal" scala way of doing thing (not withstanding the 22 parameters limit)?
As can be seen, I'm very new to all this.
At first the basic option of Tony K.:
def withName(n : String) = Person(n, address)
looked promising, but I have quite a few classes that extends PersonInfo.
That means in each one I would have to re-implement the defs, lots of typing and cutting and pasting,
just to do something simple.
If I convert the trait PersonInfo to a normal class and put all the defs in it, then
I have the problem of how can I return a Person, not a PersonInfo?
Is there a clever scala thing to somehow implement in the trait or super class and have
all subclasses really extend?
As far as I can see all works very well in scala when the examples are very simple,
2 or 3 parameters, but when you have dozens it becomes very tedious and unworkable.
PersonContext of weirdcanada is I think similar, still thinking about this one. I guess if
I have 43 parameters I would need to breakup into multiple temp classes just to pump
the parameters into Person.
The copy option is also interesting, cryptic but a lot less typing.
Coming from java I was hoping for some clever tricks from scala.
Case classes have a pre-defined copy method which you should use for this.
case class Person(name: String, age: Int)
val mike = Person("Mike", 42)
val newMike = mike.copy(age = 43)
How does this work? copy is just one of the methods (besides equals, hashCode etc) that the compiler writes for you. In this example it is:
def copy(name: String = name, age: Int = age): Person = new Person(name, age)
The values name and age in this method shadow the values in the outer scope. As you can see, default values are provided, so you only need to specify the ones that you want to change. The others default to what there are in the current instance.
The reason for the existence of var in scala is to support mutable state. In some cases, mutable state is truly what you want (e.g. for performance or clarity reasons).
You are correct, though, that there is much evidence and experience behind the encouragement to use immutable state. Things work better on many fronts (concurrency, clarity of reason, etc).
One answer to your question is to provide mutator methods to the class in question that don't actually mutate the state, but instead return a new object with a modified entry:
case class Person(val name : String, val address : String) {
def withName(n : String) = Person(n, address)
...
}
This particular solution does involve coding potentially long parameter lists, but only within the class itself. Users of it get off easy:
val p = Person("Joe", "N St")
val p2 = p.withName("Sam")
...
If you consider the reasons you'd want to mutate state, then thing become clearer. If you are reading data from a database, you could have many reasons for mutating an object:
The database itself changed, and you want to auto-refresh the state of the object in memory
You want to make an update to the database itself
You want to pass an object around and have it mutated by methods all over the place
In the first case, immutable state is easy:
val updatedObj = oldObj.refresh
The second is much more complex, and there are many ways to handle it (including mutable state with dirty field tracking). It pays to look at libraries like Squery, where you can write things in a nice DSL (see http://squeryl.org/inserts-updates-delete.html) and avoid using the direct object mutation altogether.
The final one is the one you generally want to avoid for reasons of complexity. Such things are hard to parallelize, hard to reason about, and lead to all sorts of bugs where one class has a reference to another, but no guarantees about the stability of it. This kind of usage is the one that screams for immutable state of the form we are talking about.
Scala has adopted many paradigms from Functional Programming, one of them being a focus on using objects with immutable state. This means moving away from getters and setters within your classes and instead opting to to do what #Tony K. above has suggested: when you need to change the "state" of an inner object, define a function that will return a new Person object.
Trying to use immutable objects is likely the preferred Scala way.
In regards to the 22 parameter issue, you could create a context class that is passed to the constructor of Person:
case class PersonContext(all: String, of: String, your: String, parameters: Int)
class Person(context: PersonContext) extends PersonInfo { ... }
If you find yourself changing an address often and don't want to have to go through the PersonContext rigamarole, you can define a method:
def addressChanger(person: Person, address: String): Person = {
val contextWithNewAddress = ...
Person(contextWithNewAddress)
}
You could take this even further, and define a method on Person:
class Person(context: PersonContext) extends PersonInfo {
...
def newAddress(address: String): Person = {
addressChanger(this, address)
}
}
In your code, you just need to make remember that when you are updating your objects that you're often getting new objects in return. Once you get used to that concept, it becomes very natural.