Scala Stream tail laziness and synchronization - multithreading

Question

In one of his videos (concerning Scala's lazy evaluation, namely lazy keyword), Martin Odersky shows the following implementation of cons operation used to construct a Stream:
def cons[T](hd: T, tl: => Stream[T]) = new Stream[T] {
def head = hd
lazy val tail = tl
...
}
So tail operation is written concisely using lazy evaluation feature of the language.
But in reality (in Scala 2.11.7), the implementation of tail is a bit less elegant:
#volatile private[this] var tlVal: Stream[A] = _
#volatile private[this] var tlGen = tl _
def tailDefined: Boolean = tlGen eq null
override def tail: Stream[A] = {
if (!tailDefined)
synchronized {
if (!tailDefined) {
tlVal = tlGen()
tlGen = null
}
}
tlVal
}
Double-checked locking and two volatile fields: that's roughly how you would implement a thread-safe lazy computation in Java.
So the questions are:
Doesn't lazy keyword of Scala provide any 'evaluated maximum once' guarantee in a multi-threaded case?
Is the pattern used in real tail implementation an idiomatic way to do a thread-safe lazy evaluation in Scala?

Answer 1

Doesn't lazy keyword of Scala provide any 'evaluated maximum once'
guarantee in a multi-threaded case?
Yes, it does, as others have stated.
Is the pattern used in real tail implementation an idiomatic way to do
a thread-safe lazy evaluation in Scala?
Edit:
I think I have the actual answer as to why not lazy val. Stream has public facing API methods such as hasDefinitionSize inherited from TraversableOnce. In order to know if a Stream has a finite size not, we need a way of checking without materializing the underlying Stream tail. Since lazy val doesn't actually expose the underlying bit, we can't do that.
This is backed by SI-1220
To strengthen this point, #Jasper-M points out that the new LazyList api in strawman (Scala 2.13 collection makeover) no longer has this issue, since the entire collection hierarchy has been reworked and there are no longer such concerns.
Performance related concerns
I would say "it depends" on which angle you're looking at this problem. From a LOB point of view, I'd say definitely go with lazy val for conciseness and clarity of implementation. But, if you look at it from the point of view of a Scala collections library author, things start to look differently. Think of it this way, you're creating a library which will be potentially be used by many people and ran on many machines across the world. This means that you should be thinking of the memory overhead of each structure, especially if you're creating such an essential data structure yourself.
I say this because when you use lazy val, by design you generate an additional Boolean field which flags if the value has been initialized, and I am assuming this is what the library authors were aiming to avoid. The size of a Boolean on the JVM is of course VM dependent, by even a byte is something to consider, especially when people are generating large Streams of data. Again, this is definitely not something I would usually consider and is definitely a micro optimization towards memory usage.
The reason I think performance is one of the key points here is SI-7266 which fixes a memory leak in Stream. Note how it is of importance to track the byte code to make sure no extra values are retained inside the generated class.
The difference in the implementation is that the definition of tail being initialized or not is a method implementation which checks the generator:
def tailDefined: Boolean = tlGen eq null
Instead of a field on the class.

Answer 2

Scala lazy values are evaluated only once in multi-threaded cases. This is because the evaluation of lazy members is actually wrapped in a synchronized block in the generated code.
Lets take a look at the simple claas,
class LazyTest {
lazy val x = 5
}
Now, lets compile this with scalac,
scalac -Xprint:all LazyTest.scala
This will result in,
package <empty> {
class LazyTest extends Object {
final <synthetic> lazy private[this] var x: Int = _;
#volatile private[this] var bitmap$0: Boolean = _;
private def x$lzycompute(): Int = {
LazyTest.this.synchronized(if (LazyTest.this.bitmap$0.unary_!())
{
LazyTest.this.x = (5: Int);
LazyTest.this.bitmap$0 = true
});
LazyTest.this.x
};
<stable> <accessor> lazy def x(): Int = if (LazyTest.this.bitmap$0.unary_!())
LazyTest.this.x$lzycompute()
else
LazyTest.this.x;
def <init>(): LazyTest = {
LazyTest.super.<init>();
()
}
}
}
You should be able to see... that the lazy evaluation is thread-safe. And you will also see some similarity to that "less elegant" implementation in Scala 2.11.7
You can also experiment with tests similar to following,
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
case class A(i: Int) {
lazy val j = {
println("calculating j")
i + 1
}
}
def checkLazyInMultiThread(): Unit = {
val a = A(6)
val futuresList = Range(1, 20).toList.map(i => Future{
println(s"Future $i :: ${a.j}")
})
Future.sequence(futuresList).onComplete(_ => println("completed"))
}
checkLazyInMultiThread()
Now, the implementation in standard library avoids using lazy because they are able to provide a more efficient solution than this generic lazy translation.

Answer 3

You are correct, lazy vals use locking precisely to guard against double evaluation when accessed at the same time by two threads. Future developments, furthermore, will give the same guarantees without locking.
What is idiomatic, in my humble opinion, is a highly debatable subject when it comes to a language that, by design, allows for a wide range of different idioms to be adopted. In general, however, application code tends to be considered idiomatic when going more into the direction of pure functional programming, as it gives a series of interesting advantages in terms of ease of testing and reasoning that would make sense to give up only in case of serious concerns. This concern can be one of performance, which is why the current implementation of the Scala Collection API, while exposing in most cases a functional interface, makes heavy use (internally and in restricted scopes) of vars, while loops and established patterns from imperative programming (as the one you highlighted in your question).