**Why is Quick Sort preferred for arrays?**

Below are recursive and iterative implementations of Quick Sort and Merge Sort for arrays.

Recursive Quick Sort for array.

Iterative Quick Sort for arrays.

Recursive Merge Sort for arrays

Iterative Merge Sort for arrays

- Quick Sort in its general form is an in-place sort (i.e. it doesn’t require any extra storage) whereas merge sort requires O(N) extra storage, N denoting the array size which may be quite expensive. Allocating and de-allocating the extra space used for merge sort increases the running time of the algorithm.
- Comparing average complexity we find that both type of sorts have O(NlogN) average complexity but the constants differ. For arrays, merge sort loses due to the use of extra O(N) storage space.
- Most practical implementations of Quick Sort use randomized version. The randomized version has expected time complexity of O(nLogn). The worst case is possible in randomized version also, but worst case doesn’t occur for a particular pattern (like sorted array) and randomized Quick Sort works well in practice.
- Quick Sort is also a cache friendly sorting algorithm as it has good locality of reference when used for arrays.
- Quick Sort is also tail recursive, therefore tail call optimizations is done.

**Why is Merge Sort preferred for Linked Lists?**

Below are implementations of Quicksort and Mergesort for singly and doubly linked lists.

Quick Sort for Doubly Linked List

Quick Sort for Singly Linked List

Merge Sort for Singly Linked List

Merge Sort for Doubly Linked List

- In case of linked lists the case is different mainly due to difference in memory allocation of arrays and linked lists. Unlike arrays, linked list nodes may not be adjacent in memory.
- Unlike array, in linked list, we can insert items in the middle in O(1) extra space and O(1) time if we are given reference/pointer to the previous node. Therefore merge operation of merge sort can be implemented without extra space for linked lists.
- In arrays, we can do random access as elements are continuous in memory. Let us say we have an integer (4-byte) array A and let the address of A[0] be x then to access A[i], we can directly access the memory at (x + i*4). Unlike arrays, we can not do random access in linked list.
- Quick Sort requires a lot of this kind of access. In linked list to access i’th index, we have to travel each and every node from the head to i’th node as we don’t have continuous block of memory. Therefore, the overhead increases for quick sort. Merge sort accesses data sequentially and the need of random access is low.

**Related Articles:**

- Why quicksort is better than mergesort ?
- Know Your Sorting Algorithm | Set 1 (Sorting Weapons used by Programming Languages)
- Iterative Merge Sort
- Iterative Quick Sort

Thanks to **Sayan Mukhopadhyay** for providing initial draft for above article. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the **DSA Self Paced Course** at a student-friendly price and become industry ready.

## Recommended Posts:

- Quick Sort vs Merge Sort
- Union and Intersection of two linked lists | Set-2 (Using Merge Sort)
- Merge Sort for Linked Lists
- Merge Sort for Linked Lists in JavaScript
- Merge Sort with O(1) extra space merge and O(n lg n) time
- Merge two sorted linked lists
- Merge two sorted linked lists such that merged list is in reverse order
- In-place Merge two linked lists without changing links of first list
- Merge K sorted linked lists | Set 1
- Merge two unsorted linked lists to get a sorted list
- Iterative Quick Sort
- C Program for Iterative Quick Sort
- Java Program for Iterative Quick Sort
- p5.js | Quick Sort
- Advanced Quick Sort (Hybrid Algorithm)
- Sorting Algorithm Visualization : Quick Sort
- Quick Sort using Multi-threading
- Visualization of Quick sort using Matplotlib
- 3D Visualisation of Quick Sort using Matplotlib in Python
- Merge operations using STL in C++ | merge(), includes(), set_union(), set_intersection(), set_difference(), ., inplace_merge,