When I had the requirement to remove duplicate items from a very large array, I found out that the classic method to be not optimised as it took a pretty long time than desired. So, I devised this new algorithm that can sort a large array in a fraction of the original time.

The fastest method to find unique items in array

This method is kind of cheeky in its implementation. It uses the JavaScript’s object to add every item in the array as key. As we all know, objects accepts only unique keys and sure we did capitalise on that.

Array.prototype.unique = function() {
    var o = {}, i, l = this.length, r = [];
    for(i=0; i<l;i+=1) o[this[i]] = this[i];
    for(i in o) r.push(o[i]);
    return r;
};

Some Thoughts On This Algorithm

This is somewhat classified as “Hash Sieving” method and can also be related to a somewhat modified “Hash Sorting Algorithm” where every item in the array is a hash value and a hash function inserts item into a bucket, replacing existing values in case of hash collision. As such, this can be applied to any programming language for faster sieving of very large arrays.

This algorithm has a linear time complexity of O(2n) in worst case scenario. This is way better than what we will observe for the classic method as described below.

About the classic method

The classic (and most popular) method of finding unique items in an array runs two loops in a nested order to compare each element with rest of the elements. Consequently, the time complexity of the classic method to find the unique items in an array is around quadratic O(n²).

This is not a good thing when you have to find unique items within array of 10,000 items.

Array.prototype.unique = function() {
    var a = [], l = this.length;
    for(var i=0; i<l; i++) {
        for(var j=i+1; j<l; j++)
            if (this[i] === this[j]) j = ++i;
        a.push(this[i]);
    }
    return a;
};

Comparing the above two algorithms

Test Data: An array of elements having N random integers.

Sample (N)	Average Case		Best Case
	Classic	New	Classic	New
50	0.43	0.25	0.01	0.02
100	0.60	0.30	0.09	0.16
500	9.57	0.87	0.1	0.2
1000	24.44	1.51	0.21	0.31
5000	584.28	7.74	0.4	1.0
10000	2360.90	15.03	0.7	1.8

Conclusion

This method of finding unique items within an array seems to be particularly useful for large arrays that are tending towards the real-life situations. When there are more items in an array that are similar, there is not much of a difference in performance and in fact, the classic algorithm scores better by a small margin. However, as the array gets more random, the runtime of the classic algorithm increases manifold.