The source code for this post is available on GitHub.

Part I discussed the Scala Stream class usage rules that help you avoid memory leaks. I will list them below for your convenience:

Define streams using def and never store them in val s. Consume streams in tail-recursive functions. Pass streams around via by-name parameters. Corollary: When defining stream-consuming functions in traits, wrap them in methods accepting streams as by-name parameters. Do not pattern match against streams outside the consuming functions. Only call the eagerly evaluated Stream methods that are marked as "optimized for GC".

It turns out, however, that rules 2 to 5 are superfluous in the presence of a precise garbage collector.

Consider rule #2:

Consume streams in tail-recursive functions.

Let’s decompile the example from Part I again:

@tailrec def sum (xs: Stream[Int], z: Int = 0 ): Int = if (xs. isEmpty ) z else sum (xs. tail , z + xs. head )

Decompiler output:

public int sum (Stream<Object> xs, int z) { for (;;) { if (xs. isEmpty ()) return z; z += BoxesRunTime. unboxToInt (xs. head ()); xs = (Stream)xs. tail (); } }

The xs parameter gets overwritten at the end of each loop iteration with the remainder of the original stream.

However, it turns out that it was perfectly possible to consume a stream in an imperative loop in the first place. Remember that, unlike in Java, in Scala function parameters are essentially val s and hence cannot be reused. So a local var has to be introduced:

def sum (xs: Stream[Int]): Int = { var scan = xs var res = 0 while (!scan. isEmpty ) { res += scan. head scan = scan. tail } res }

The xs parameter cannot be changed inside the function and therefore should hold a reference to the original stream, but, somehow, it does not?!

What happens here is that the JVM detects that the xs parameter is not used after the initial assignment to scan and therefore does not consider it being a GC root after that assignment.

Wait a minute.

Here is the example from Rule #3:

def sum (xs: Stream[Int]): Int = { @tailrec def loop (acc: Int, xs: Stream[Int]): Int = if (xs. isEmpty ) acc else loop (acc+xs. head , xs. tail ) loop ( 0 , xs) }

Here, the xs parameter of sum is also not used after the call of loop . Why does the JVM think otherwise? How is this different from the imperative implementation of sum above?

Let’s do a small experiment in the REPL:

scala> def ones = Stream.continually(1) ones: scala.collection.immutable.Stream[Int] scala> println((ones take 100000000).sum) java.lang.OutOfMemoryError: Java heap space at scala.collection.immutable.Stream$.continually(Stream.scala:1129) . . . at scala.collection.immutable.Stream.foldLeft(Stream.scala:563) at scala.collection.TraversableOnce$class.sum(TraversableOnce.scala:203) . . . scala> for (i | (ones take 10).sum | } scala> println((ones take 100000000).sum) 100000000 scala>

The first call to (ones take 100000000).sum threw an OOM error, just as anyone who’ve read the first part of this series would have expected, but the second one magically worked!

What’s going on here?

As you may see, Stream mixes in the sum implementation from the TraversableOnce trait, where it is defined as:

def sum[B >: A]( implicit num: Numeric[B]): B = foldLeft (num. zero )(num. plus )

The difference is that sum got JIT-compiled in between of the two println calls! It is the HotSpot compiler that is capable of calculating the life time of variables and parameters. In this particular case, it determines that sum ‘s receiver is not used after the foldLeft call.

The imperative version of sum contains a loop, so after some iterations the JVM considered it a "hot spot" and the JIT compiler kicked in. But even if a function itself does not contain a loop, applying it many times also triggers its JIT compilation. In the REPL session shown above, sum gets applied to ten-element streams 10,000 times, which happens to be the default threshold for the HotSpot Server VM (for the Client VM it is just 1,500).

That is the "magic" that causes sum to stop leaking memory. And of course, it would not have leaked memory at all if JIT compilation was forced using the HotSpot -Xcomp option, or if it was run on a JVM with a precise GC and no interpreter at all.

In fact, all "faulty" tests for Rules #2-5 pass on HotSpot with -Xcomp .

Which means that defining stream-consuming functions in traits makes no difference if the forwarders get JIT-compiled.

And also that the non-specialized TraversableOnce methods do not actually leak memory, but it takes an optimizing compiler working in collaboration with a precise GC to recognize that.

As far as pattern matching is concerned, you still have to make sure that pattern variables are not used after the call of a stream-consuming function. As you saw in Part I, those variables are implicit val s, and Rule #1 holds the sophisticatedness of the underlying JVM notwithstanding.

For instance, the following function leaks memory regardless of whether -Xcomp is present:

def tailAvg (xs: Stream[Int]): Option[Int] = { xs match { case Stream. Empty => None case y #:: Stream. Empty => None case y #:: ys => Some(ys. sum / ys. length ) } }

Square Peg, Round Hole

As I dug through the peculiarities of Scala implementation and observed their interference with HotSpot optimizations, it has grown on me that using "infinite" Scala Stream s in production code is an inherently bad idea. After all, Stream is memoizing by design; it is designed to be a lazy equivalent of List , and we’ve been trying to circumvent the intent of its authors!

That said, using the standard Stream class to illustrate the concept of potentially infinite data structures in the context of an academic exercise is probably fine. All code is under your total control, and usually there is not that much code, so sticking to The Rules and/or enforcing JIT compilation is not hard. But the teachers better warn their students against applying this particular knowledge in production, because:

You would normally want your production code to be JVM-agnostic, especially if you are creating a library or framework that other people will use in arbitrary contexts and environments.

Without tool support, enforcing any sophisticated coding rules throughout the lifetime of a project larger than a student assignment is next to impossible.

The authors of third-party libraries and legacy code are likely to be unaware of these rules.

For instance, consider the following scenario: suppose your code, or a third-party library you are using, breaks one of the "optional" rules, but all your load tests trigger JIT compilation of the respective classes, one way or the other. Effectively, you will be shipping an app with a latent memory leak, isolating which may be quite tricky.

So, if Stream is not the solution, what are the alternatives?

There are two options that I am aware of, and I will consider them in Parts III and IV. Stay tuned!

Update 11-Aug-2014: Part III is available.

Tags: garbage collection, scala, streams