Convert Traversable[T] to Stream[T] without traversing or stack overflow - scala

Question

I am using a library that provides a Traversable[T] that pages through database results. I'd like to avoid loading the whole thing into memory, so I am trying to convert it to a Stream[T].
From what I can tell, the built in "asStream" method loads the whole Traversable into a Buffer, which defeats my purpose. My attempt (below) hits a StackOverflowException on large results, and I can't tell why. Can someone help me understand what is going on? Thanks!
def asStream[T](traversable: => Traversable[T]): Stream[T] = {
if (traversable.isEmpty) Empty
else {
lazy val head = traversable.head
lazy val tail = asStream(traversable.tail)
head #:: tail
}
}
Here's a complete example that reproduces this, based on a suggestion by #SCouto
import scala.collection.immutable.Stream.Empty
object StreamTest {
def main(args: Array[String]) = {
val bigVector = Vector.fill(90000)(1)
val optionStream = asStream(bigVector).map(v => Some(v))
val zipped = optionStream.zipAll(optionStream.tail, None, None)
}
def asStream[T](traversable: => Traversable[T]): Stream[T] = {
#annotation.tailrec
def loop(processed: => Stream[T], pending: => Traversable[T]): Stream[T] = {
if (pending.isEmpty) processed
else {
lazy val head = pending.head
lazy val tail = pending.tail
loop(processed :+ head, tail)
}
}
loop(Empty, traversable)
}
}
Edit: After some interesting ideas from #SCouto, I learned this could also be done with trampolines to keep the result as a Stream[T] that is in the original order
object StreamTest {
def main(args: Array[String]) = {
val bigVector = Range(1, 90000).toVector
val optionStream = asStream(bigVector).map(v => Some(v))
val zipped = optionStream.zipAll(optionStream.tail, None, None)
zipped.take(10).foreach(println)
}
def asStream[T](traversable: => Traversable[T]): Stream[T] = {
sealed trait Traversal[+R]
case class More[+R](result: R, next: () => Traversal[R]) extends Traversal[R]
case object Done extends Traversal[Nothing]
def next(currentTraversable: Traversable[T]): Traversal[T] = {
if (currentTraversable.isEmpty) Done
else More(currentTraversable.head, () => next(currentTraversable.tail))
}
def trampoline[R](body: => Traversal[R]): Stream[R] = {
def loop(thunk: () => Traversal[R]): Stream[R] = {
thunk.apply match {
case More(result, next) => Stream.cons(result, loop(next))
case Done => Stream.empty
}
}
loop(() => body)
}
trampoline(next(traversable))
}
}

Answer 1

Try this:
def asStream[T](traversable: => Traversable[T]): Stream[T] = {
#annotation.tailrec
def loop(processed: Stream[T], pending: Traversable[T]): Stream[T] = {
if (pending.isEmpty) processed
else {
lazy val head = pending.head
lazy val tail = pending.tail
loop(head #:: processed, tail)
}
}
loop(Empty, traversable)
}
The main point is to ensure that your recursive call is the last action of your recursive function.
To ensure this you can use both a nested method (called loop in the example) and the tailrec annotation which ensures your method is tail-safe.
You can find info about tail rec here and in this awesome answer here
EDIT
The problem was that we were adding the element at the end of the Stream. If you add it as head of the Stream as in your example it will work fine. I updated my code. Please test it and let us know the result.
My tests:
scala> val optionStream = asStream(Vector.fill(90000)(1)).map(v => Some(v))
optionStream: scala.collection.immutable.Stream[Some[Int]] = Stream(Some(1), ?)
scala> val zipped = optionStream.zipAll(optionStream.tail, None, None)
zipped: scala.collection.immutable.Stream[(Option[Int], Option[Int])] = Stream((Some(1),Some(1)), ?)
EDIT2:
According to your comments, and considering the fpinscala example as you said. I think this may help you. The point is creating a case class structure with lazy evaluation. Where the head is a single element, and the tail a traversable
sealed trait myStream[+T] {
def head: Option[T] = this match {
case MyEmpty => None
case MyCons(h, _) => Some(h())
}
def tail: myStream[T] = this match {
case MyEmpty => MyEmpty
case MyCons(_, t) => myStream.cons(t().head, t().tail)
}
}
case object MyEmpty extends myStream[Nothing]
case class MyCons[+T](h: () => T, t: () => Traversable[T]) extends myStream[T]
object myStream {
def cons[T](hd: => T, tl: => Traversable[T]): myStream[T] = {
lazy val head = hd
lazy val tail = tl
MyCons(() => head, () => tail)
}
def empty[T]: myStream[T] = MyEmpty
def apply[T](as: T*): myStream[T] = {
if (as.isEmpty) empty
else cons(as.head, as.tail)
}
}
Some Quick tests:
val bigVector = Vector.fill(90000)(1)
myStream.cons(bigVector.head, bigVector.tail)
res2: myStream[Int] = MyCons(<function0>,<function0>)
Retrieving head:
res2.head
res3: Option[Int] = Some(1)
And the tail:
res2.tail
res4: myStream[Int] = MyCons(<function0>,<function0>)
EDIT3
The trampoline solution by the op:
def asStream[T](traversable: => Traversable[T]): Stream[T] = {
sealed trait Traversal[+R]
case class More[+R](result: R, next: () => Traversal[R]) extends Traversal[R]
case object Done extends Traversal[Nothing]
def next(currentTraversable: Traversable[T]): Traversal[T] = {
if (currentTraversable.isEmpty) Done
else More(currentTraversable.head, () => next(currentTraversable.tail))
}
def trampoline[R](body: => Traversal[R]): Stream[R] = {
def loop(thunk: () => Traversal[R]): Stream[R] = {
thunk.apply match {
case More(result, next) => Stream.cons(result, loop(next))
case Done => Stream.empty
}
}
loop(() => body)
}
trampoline(next(traversable))
}
}

Answer 2

Stream doesn't keep the data in memory because you declare how to generate each item. It's very likely that your database data is not been procedurally generated so what you need is to fetch the data the first time you ask for it (something like def getData(index: Int): Future[Data]).
The biggest problem rise in, since you are fetching data from a database, you are probably using Futures so, even if you are able to achieve it, you would have a Future[Stream[Data]] object which is not that nice to use or, much worst, block it.
Wouldn't be much more worthy just to paginate your database data query?