Question

I sense that the Scala community has a little big obsession with writing “concise”, “cool”, “scala idiomatic”, “one-liner” -if possible- code. This is immediately followed by a comparison to Java/imperative/ugly code.

While this (sometimes) leads to easy to understand code, it also leads to inefficient code for 99% of developers. And this is where Java/C++ is not easy to beat.

Consider this simple problem: Given a list of integers, remove the greatest element. Ordering does not need to be preserved.

Here is my version of the solution (It may not be the greatest, but it’s what the average non-rockstar developer would do).

def removeMaxCool(xs: List[Int]) = {
  val maxIndex = xs.indexOf(xs.max);
  xs.take(maxIndex) ::: xs.drop(maxIndex+1)
}

It’s Scala idiomatic, concise, and uses a few nice list functions. It’s also very inefficient. It traverses the list at least 3 or 4 times.

Here is my totally uncool, Java-like solution. It’s also what a reasonable Java developer (or Scala novice) would write.

def removeMaxFast(xs: List[Int]) = {
    var res = ArrayBuffer[Int]()
    var max = xs.head
    var first = true;   
    for (x <- xs) {
        if (first) {
            first = false;
        } else {
            if (x > max) {
                res.append(max)
                max = x
            } else {
                res.append(x)
            }
        }
    }
    res.toList
}

Totally non-Scala idiomatic, non-functional, non-concise, but it’s very efficient. It traverses the list only once!

So, if 99% of Java developers write more efficient code than 99% of Scala developers, this is a huge
obstacle to cross for greater Scala adoption. Is there a way out of this trap?

I am looking for practical advice to avoid such “inefficiency traps” while keeping implementation clear ans concise.

Clarification: This question comes from a real-life scenario: I had to write a complex algorithm. First I wrote it in Scala, then I “had to” rewrite it in Java. The Java implementation was twice as long, and not that clear, but at the same time it was twice as fast. Rewriting the Scala code to be efficient would probably take some time and a somewhat deeper understanding of scala internal efficiencies (for vs. map vs. fold, etc)

Answer 1

Let’s discuss a fallacy in the question:

So, if 99% of Java developers write more efficient code than 99% of
Scala developers, this is a huge obstacle to cross for greater Scala
adoption. Is there a way out of this trap?

This is presumed, with absolutely no evidence backing it up. If false, the question is moot.

Is there evidence to the contrary? Well, let’s consider the question itself — it doesn’t prove anything, but shows things are not that clear.

Totally non-Scala idiomatic, non-functional, non-concise, but it’s
very efficient. It traverses the list only once!

Of the four claims in the first sentence, the first three are true, and the fourth, as shown by user unknown, is false! And why it is false? Because, contrary to what the second sentence states, it traverses the list more than once.

The code calls the following methods on it:

res.append(max)
res.append(x)

and

res.toList

Let’s consider first append.

1. append takes a vararg parameter. That means max and x are first encapsulated into a sequence of some type (a WrappedArray, in fact), and then passed as parameter. A better method would have been +=.

2. Ok, append calls ++=, which delegates to +=. But, first, it calls ensureSize, which is the second mistake (+= calls that too — ++= just optimizes that for multiple elements). Because an Array is a fixed size collection, which means that, at each resize, the whole Array must be copied!

So let’s consider this. When you resize, Java first clears the memory by storing 0 in each element, then Scala copies each element of the previous array over to the new array. Since size doubles each time, this happens log(n) times, with the number of elements being copied increasing each time it happens.

Take for example n = 16. It does this four times, copying 1, 2, 4 and 8 elements respectively. Since Java has to clear each of these arrays, and each element must be read and written, each element copied represents 4 traversals of an element. Adding all we have (n – 1) * 4, or, roughly, 4 traversals of the complete list. If you count read and write as a single pass, as people often erroneously do, then it’s still three traversals.

One can improve on this by initializing the ArrayBuffer with an initial size equal to the list that will be read, minus one, since we’ll be discarding one element. To get this size, we need to traverse the list once, though.

Now let’s consider toList. To put it simply, it traverses the whole list to create a new list.

So, we have 1 traversal for the algorithm, 3 or 4 traversals for resize, and 1 additional traversal for toList. That’s 4 or 5 traversals.

The original algorithm is a bit difficult to analyse, because take, drop and ::: traverse a variable number of elements. Adding all together, however, it does the equivalent of 3 traversals. If splitAt was used, it would be reduced to 2 traversals. With 2 more traversals to get the maximum, we get 5 traversals — the same number as the non-functional, non-concise algorithm!

So, let’s consider improvements.

On the imperative algorithm, if one uses ListBuffer and +=, then all methods are constant-time, which reduces it to a single traversal.

On the functional algorithm, it could be rewritten as:

val max = xs.max
val (before, _ :: after) = xs span (max !=)
before ::: after

That reduces it to a worst case of three traversals. Of course, there are other alternatives presented, based on recursion or fold, that solve it in one traversal.

And, most interesting of all, all of these algorithms are O(n), and the only one which almost incurred (accidentally) in worst complexity was the imperative one (because of array copying). On the other hand, the cache characteristics of the imperative one might well make it faster, because the data is contiguous in memory. That, however, is unrelated to either big-Oh or functional vs imperative, and it is just a matter of the data structures that were chosen.

So, if we actually go to the trouble of benchmarking, analyzing the results, considering performance of methods, and looking into ways of optimizing it, then we can find faster ways to do this in an imperative manner than in a functional manner.

But all this effort is very different from saying the average Java programmer code will be faster than the average Scala programmer code — if the question is an example, that is simply false. And even discounting the question, we have seen no evidence that the fundamental premise of the question is true.

EDIT

First, let me restate my point, because it seems I wasn’t clear. My point is that the code the average Java programmer writes may seem to be more efficient, but actually isn’t. Or, put another way, traditional Java style doesn’t gain you performance — only hard work does, be it Java or Scala.

Next, I have a benchmark and results too, including almost all solutions suggested. Two interesting points about it:

1. Depending on list size, the creation of objects can have a bigger impact than multiple traversals of the list. The original functional code by Adrian takes advantage of the fact that lists are persistent data structures by not copying the elements right of the maximum element at all. If a Vector was used instead, both left and right sides would be mostly unchanged, which might lead to even better performance.

2. Even though user unknown and paradigmatic have similar recursive solutions, paradigmatic’s is way faster. The reason for that is that he avoids pattern matching. Pattern matching can be really slow.

The benchmark code is here, and the results are here.