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Difference between merge sort and quick sort

Sorting is organizing a group of things or pieces in a specific order. Depending on specific criteria, such as numerical values, alphabetical sequences, or other sets of comparisons, the ordering can vary between ascending and descending. Classification represents a core operation in computer science, efficiently retrieving information, analyzing data, performing searches, and structuring data in various applications. Numerous classification systems exist, each with unique benefits and drawbacks, including differences in time complexity, computational complexity, memory utilization, and suitability for optimizing various input arrays.

Merge Sort

Merge sorting is a popular and efficient sorting algorithm in the divide-and-conquer class. It takes an unsorted list, divides it into smaller sublists, sorts those sublists repeatedly, and then recombines them into a sorted list. The basic principle of a combining process is to divide the list until you find subsets of only one or zero entries that are trivially sorted. Then those subsets are combined again to form the combined list.

Here is the high-level information about the merge-sort algorithm:

Divide: An unsorted list is divided into two equal (or approximate if the number of elements is odd) halves.

Conquer: Apply the merge sort algorithm recursively to each sublist created in the previous step until you no longer have sublists of size 1 or 0 (already sorted).

Merge: Recombines sorted sublists into a single sorted list. This merging process ensures that the elements are arranged in the correct order.

Quick sort

Quick Sort is a popular sorting algorithm that employs the "divide and conquer" strategy. It comprises splitting an array into two sub-arrays, one with elements less than a defined pivot and the other with elements greater than the pivot. The pivot then assumes its ultimate location, and the method is repeated on the two sub-arrays. Quick Sort is frequently quicker than other sorting algorithms, such as merge sort and insertion sort, with an average time complexity of O(n log n). However, Quick Sort's speed depends on pivot selection, which may impose costs.

Here is a step-by-step explanation of how Quick Sort works:

Pivot Selection: Select the pivot element from the array. The choice of pivots can be different, and the different ways pivots are chosen can affect the algorithm's performance.

Splitting: Rearrange the array elements so that all elements less than the pivot are in front of the pivot, and all elements greater than the pivot are behind. Then, the pivot is at the last sorted position. This classification step is performed in linear time.

Repeat: Apply the Quick Sort algorithm recursively to the left of the pivot (it has fewer elements than the pivot) and to the subarrays to the right (which contain more elements than the pivot). Continue this process until the subarray size is 0 or 1, as configured in this context. You believe.

Combination: There is no explicit "Combine" step in Quick Sort because the array is sorted by location in the split step. Sequenced subarrays are implicitly combined.

Difference between quick sort and merge sort

Parameters Quick sort Merge sort
Stability Quick sort is not a stable sorting method. Hence, the relative rank of equal elements may fluctuate. Merge sort is a stable sorting algorithm.
Time complexity O(n^2) O(nlogn)
Algorithm Quick sort is a partition-exchange method, which divides the array and swaps items based on a pivot. Merge sort is a divide-and-conquer method that splits the array in half until it finds a base case.
Linked list Quick Sort could be better for sorting linked lists since it needs element access at random. Merge sort is appropriate for sorting linked lists since it can quickly combine two sorted lists.
Sorting method It is an internal sorting mechanism to sort the array or data stored in the main memory. An external sorting technique sorts arrays or data sets from an external file.
Applications DBMS File merging
Use cases Quick Sort is frequently used when efficiency is a top goal, especially for smaller datasets, and memory utilization should be minimal. It's also utilized when a stable sort isn't required. Merge Sort is a solid solution when stability and consistent performance are needed or memory utilization is not a crucial concern.


Although merge sort and fast Sort are efficient sorting algorithms, their approaches, time complexity, partitioning step, adaptive behavior, and applications differ. The unique needs of the sorting operation determine the decision between the two. Understanding these distinctions will assist developers in making educated judgments when picking the best algorithm for their apps.

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