Author
Dipen Dadhaniya
Engineering Manager at Interview Kickstart
Are you getting ready for your next technical interview? As a software developer, you know how important it is to have a crystal clear understanding of sorting algorithms. They’re a frequent feature in coding interviews! Let’s brush up on your sorting basics.
In this article, we will learn the difference between the two most-used sorting algorithms — merge sort and quicksort:
Merge sort is a comparison-based sorting algorithm that employs the divide-and-conquer strategy. In the divide-and-conquer approach, the problem is divided into multiple subproblems, solved individually, and finally, the result of the subproblems are combined to form the final solution.
In merge sort, we divide the array into two smaller subarrays of equal size or with a size difference of one, depending on the parity of the array’s length. Each subarray is further divided into two smaller subarrays again and again recursively until we get subarrays of size one. We then sort the subarrays and merge them to produce the sorted array.
The basic logic flows as follows:
Let’s take an example to see how it works:
We now merge each sorted list together with its neighbors — maintaining sorted order.
Check out the “Merge Sort Algorithm†article for a detailed explanation with pseudocode and code.
Quicksort is a comparison-based sorting algorithm. Like merge sort, this is also based on the divide-and-conquer strategy. The algorithm has two basic operations — swapping items in place and partitioning a section of the array.
Quicksort sorts an array by choosing a pivot element and then partitioning the rest of the elements around the pivot. All the elements less than the pivot are moved to the left side of the pivot (called left partition), and the elements greater than or equal to the pivot are moved to the right of the pivot (called right partition).
The sorting is continued on left and right partitions separately and recursively by choosing pivot points and breaking down the partitions into single-element subarrays before combining them to form one sorted list.
The choice of the pivot plays a significant role in how efficiently the quicksort works in general. There are many ways to choose the pivot:
Let’s take an example to see one round of quicksort:
Given array = {40, 21, 8, 17, 51, 34}
Sorted array = {8, 17, 21, 34, 40, 51}
Have a look at the article “Quicksort Algorithm†for a detailed explanation of the algorithm along with pseudocode and code.
Although both merge sort and quicksort work on the same divide-and-conquer principle, they handle the partition and sorting very differently.
Merge sort partitions a list into two sublists of equal sizes (different in size by 1, when the size of the list is odd) and merges the sorted sublists optimally to form a sorted list. In contrast, quicksort doesn’t necessarily partition the list into sublists of equal size. The partition sizes may be of any size, depending on how we choose the pivot.
We can also observe that merge sort performs all the sorting during the process of merging while quicksort performs most of the sorting in the process of dividing.
Here’s a sheet for a quick overview of the differences between merge sort and quicksort for interview prep:
Question 1: Which is a better sorting algorithm — merge sort or quicksort?
Answer: There’s no definite answer to this question. It really depends on the kind of data we want to sort and what kind of sorting we expect. Both the algorithms have their advantages and disadvantages.
Let’s just go through some scenarios for better understanding:
Question 2: Why is merge sort preferred over quicksort for sorting linked lists?
Answer: Quicksort highly depends on randomly accessing the data elements and swap elements in the dataset. Since the memory allocation of linked lists is not necessarily continuous, we can not randomly access elements of a linked list efficiently. This also makes swapping very expensive in linked lists. Merge sort is faster in this situation because it reads the data sequentially. Data insertion in any part of the linked list is also very efficient if we are given the reference to the previous node so that the merge operation can be implemented in-place. Thus, merge sort becomes an ideal sorting algorithm for linked lists.
Sorting algorithms interview questions feature in almost every coding interview for software developers. If you’re looking for guidance and help to nail these questions and more, sign up for our free webinar.
As pioneers in the field of technical interview prep, we have trained thousands of software engineers to crack the toughest coding interviews and land jobs at their dream companies, such as Google, Facebook, Apple, Netflix, Amazon, and more!
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Article contributed by Shivam Gupta
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