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Last updated on 2024年8月8日

What are the differences between merge sort and quick sort algorithms?

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If you are learning programming, you might have encountered two popular sorting algorithms: merge sort and quick sort. These algorithms are widely used to sort arrays or lists of data efficiently and elegantly. But how do they work and what are the differences between them? In this article, you will learn the basic idea, the steps, the advantages, and the disadvantages of both algorithms.

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Rahul Singh

Sr. SDE & Leadership @Edzola || ML/AI @IIITB '25 || Stocks || LeetCoder || St. Xaviers, Mumbai || Ruia
Payal Shinde

Python || Cloud Computing || Javascript || HTML || CSS || problem solving || Angular || Node js

1 Merge sort

Merge sort is a divide-and-conquer algorithm that splits the input array into smaller subarrays until they have only one element each. Then, it merges these subarrays back together in sorted order by comparing the first elements of each pair of subarrays and taking the smaller one. This process is repeated until the entire array is sorted. For example, if you want to sort the array [5, 2, 4, 7, 1, 3, 6], merge sort would split it into [5, 2, 4] and [7, 1, 3, 6], then into [5], [2], [4] and [7], [1], [3], [6], and then merge them back together as [2, 5], [4], [1, 7], [3, 6], and finally as [1, 2, 3, 4, 5, 6, 7].

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Payal Shinde

Python || Cloud Computing || Javascript || HTML || CSS || problem solving || Angular || Node js
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Merge sort and quick sort are both efficient, divide-and-conquer sorting algorithms but differ in approach and performance. Merge sort splits the array into halves, recursively sorts them, and merges the sorted halves, ensuring a consistent O(n log n) time complexity but requiring O(n) extra space. Quick sort, on the other hand, selects a pivot, partitions the array around the pivot, and recursively sorts the partitions. It has an average and best-case time complexity of O(n log n) but can degrade to O(n2) in the worst case, though it typically requires less space (O(log n) due to recursion).

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Chanaput Saetang

Software Test Engineer | Data Engineer
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Actually merge sort is good with the small amount of data but in some cases there is more efficiency than merge sort algorithm in some scenarios.

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Walid Khalfa

ISTQB Certified QA Engineer | Aspiring DevOps Specialist | Software Test Automation Enthusiast | Ensuring Quality Through Innovation and Precision
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Merge sort and quick sort, both divide-and-conquer algorithms, differ in their approaches and performance. Merge sort divides the list into sublists and merges them in sorted order, guaranteeing O(n log n) time complexity but requiring additional memory for merging. Quick sort selects a pivot and partitions the list around it, often achieving faster average performance but susceptible to a worst-case O(n^2) complexity with poor pivot choices. Merge sort is stable, preserving the relative order of equal elements, while quick sort is not inherently stable. The choice between the two depends on factors like data size, memory constraints, and the need for stability.

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2 Quick sort

Quick sort is another divide-and-conquer algorithm that partitions the input array into two subarrays based on a pivot element. The pivot element is chosen randomly or by some heuristic, and all the elements that are smaller than the pivot are moved to the left of it, while all the elements that are larger than the pivot are moved to the right of it. This way, the pivot element is in its correct position in the sorted array. Then, quick sort recursively applies the same procedure to the left and right subarrays until they are sorted. For example, if you want to sort the same array [5, 2, 4, 7, 1, 3, 6], quick sort might choose 4 as the pivot and partition the array into [2, 1, 3, 4] and [5, 7, 6], then sort each subarray separately.

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Chanaput Saetang

Software Test Engineer | Data Engineer
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Quick sort is efficient when data are randomly open and a small amount but in some cases if data is big is almost the least efficient type of sort.

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3 Advantages of merge sort

Merge sort has some advantages over quick sort. One of them is that merge sort is stable, meaning that it preserves the relative order of equal elements in the input array. This can be important if you are sorting objects that have multiple attributes and you want to maintain their original order. Another advantage of merge sort is that it has a consistent time complexity of O(n log n), where n is the number of elements in the array. This means that merge sort always performs well regardless of the input data.

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4 Advantages of quick sort

Quick sort also has some advantages over merge sort. One of them is that quick sort is usually faster than merge sort in practice, because it has a lower constant factor in its time complexity. This means that quick sort can sort the same amount of data with fewer operations than merge sort. Another advantage of quick sort is that it is in-place, meaning that it does not require extra space to store the subarrays. This can save memory and improve performance.

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5 Disadvantages of merge sort

Merge sort also has some disadvantages compared to quick sort. One of them is that merge sort requires extra space to store the subarrays that are created during the splitting and merging process. This can be a problem if you are sorting a large amount of data or if you have limited memory. Another disadvantage of merge sort is that it is not adaptive, meaning that it does not take advantage of the existing order in the input array. This can lead to unnecessary comparisons and movements of elements.

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6 Disadvantages of quick sort

Quick sort also has some disadvantages compared to merge sort. One of them is that quick sort is unstable, meaning that it can change the relative order of equal elements in the input array. This can be a problem if you are sorting objects that have multiple attributes and you want to keep their original order. Another disadvantage of quick sort is that it has a worst-case time complexity of O(n^2), where n is the number of elements in the array. This means that quick sort can perform poorly if the input data is already sorted or nearly sorted, or if the pivot element is chosen poorly.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rahul Singh

Sr. SDE & Leadership @Edzola || ML/AI @IIITB '25 || Stocks || LeetCoder || St. Xaviers, Mumbai || Ruia
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?????????? ????????: Ideal for large datasets, especially when sorting data that doesn't fit into memory. It’s stable, with a consistent O(nlogn) time complexity. Example: Sorting a 10GB log file on disk by breaking it into smaller chunks, sorting each in memory, and merging them. ?????????? ????????: Best for small to medium in-memory datasets due to its average O(nlogn) performance and low overhead. However, it can degrade to O(n2) with poor pivot selection, so randomizing pivots is common. Example: Sorting an array of 10,000 user scores in an online game quickly and efficiently. Summary: Use Merge Sort for large, external datasets, and Quick Sort for fast, in-memory sorting of moderate-sized data.

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What are the differences between merge sort and quick sort algorithms?

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3

4

5

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7

1 Merge sort

2 Quick sort

3 Advantages of merge sort

4 Advantages of quick sort

5 Disadvantages of merge sort

6 Disadvantages of quick sort

7 Here’s what else to consider

Programming

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What are the differences between merge sort and quick sort algorithms?

1

2

3

4

5

6

7

1 Merge sort

2 Quick sort

3 Advantages of merge sort

4 Advantages of quick sort

5 Disadvantages of merge sort

6 Disadvantages of quick sort

7 Here’s what else to consider

Programming

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