This post evaluates the performance of Scala tail recursion comparatively to iterative methods

**Overview**

In Scala, the

*tail recursion*is a commonly used technique to apply a transformation to the elements of a collection. The purpose of this post is to evaluate the performance degradation of the tail recursion comparatively to iterative based methods.

**Note**: For the sake of readability of the implementation of algorithms, all non-essential code such as error checking, comments, exception, validation of class and method arguments, scoping qualifiers or import is omitted**Test Benchmark**

let's consider a "recursive" data transformation on an array using a sliding window. For the sake of simplicity, we create a simple polynomial transform on a array of values

{X0, ... ,Xn, ... Xp}with a window w, defined as

f(Xn) = (n-1)Xn-1 + (n-2)Xn-2 + ... + (n-w)Xn-wSuch algorithms are widely used in signal processing and technical analysis of financial markets (i.e. moving average, filters).

def polynomial(values: Array[Int]): Int = (if(values.size < W_SIZE) values else values.takeRight(W_SIZE) ).sum

The first implementation of the polynomial transform is a tail recursion on each element Xn of the array. The transform f compute

*f (values(cursor) )*from the array*values[0, ... , cursor-1]*as describe in the code snippet below1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | class Evaluation(values: Array[Int]) { def recurse(f: Array[Int] => Int): Array[Int] = { @scala.annotation.tailrec def recurse( f: Array[Int] => Int, cursor: Int, results: Array[Int]): Boolean = { if( cursor >= values.size) // exit condition true else { val arr = f(values.slice(cursor+1, cursor-W_SIZE)) results.update(cursor, arr) recurse(f, cursor+1, results) } } val results = new Array[Int](values.size) recurse(f, 0, results) results } } |

The second implementation relies on the

*scanLeft*method that return a cumulative of transformed value f(Xn).def scan(f: Array[Int] => Int): Array[Int] = values.zipWithIndex.scanLeft(0)((sum, vn) => f(values.slice(vn._2+1, vn._2-W_SIZE)) )

Finally, we implement the polynomial transform on the sliding array window with a

*map*method.def map(f: Array[Int] => Int): Array[Int] = values.zipWithIndex.map(vn => f(values.slice(vn._2+1, vn._2-W_SIZE)) )

**Results**

For the test, each of those 3 methods is executed 1000 on a dual core i7 with 8 Gbyte RAM and MacOS X Mountain Lion 10.8. The first test consists of executing the 3 methods and varying the size of the array from 10 to 90. The test is repeated 5 times and the duration is measured in milliseconds.

The tail recursion is significantly faster than the two other methods. The scan methods (scan, scanLeft, scanRight) have significant overhead that cannot be "amortized" over a small array. It is worth noticing that the performance of

*map*and*scan*are similar. The relative performance of those 3 methods is confirmed while testing with large size array (from 1,000,000 to 9,000,000 items).**References**

Programming in Scala M. Odersky, L. Spoon, B. Venners - Artima 2010

Scala for the Impatient - C. Horstman - Addison-Wesley - 2012

Scala for Machine Learning - Patrick Nicolas - Packt Publishing