Quick Sort vs Merge Sort: Which Algorithm Is Faster?

Sorting algorithms are at the heart of efficient data processing, and two of the most commonly discussed are Quick Sort and Merge Sort. If you’ve ever wondered “Which sorting algorithm should I use?”, this article is for you.

In this comprehensive guide, we’ll explore the core differences between Quick Sort and Merge Sort — including their time and space complexity, stability, use cases, and performance in JavaScript and Python. Whether you’re prepping for a coding interview or choosing the right sort for a real-world project, we’ve got you covered.

What is Merge Sort?
What is Quick Sort?
Merge Sort vs. Quick Sort: Key Differences
- 3.1. Time Complexity
- 3.2. Space Complexity
- 3.3. Stability
- 3.4. Performance in Practice
- 3.5. Use Cases
Code Snippets: Merge Sort in JavaScript and Python
Code Snippets: Quick Sort in JavaScript and Python
When to Use Merge Sort and Quick Sort
Conclusion: Which One to Choose?

What is Merge Sort?

Merge Sort is a comparison-based, stable sorting algorithm that works on the divide-and-conquer principle. The algorithm divides the array into two halves, recursively sorts each half, and then merges the two sorted halves into a final sorted array. The key advantage of Merge Sort is its predictable time complexity and stability.

Merge Sort: Algorithm Steps

Divide the array into two halves.
Recursively sort both halves.
Merge the two halves into a sorted array.

Merge Sort: Time and Space Complexity

Time Complexity: O(n log n) (Best, Worst, and Average Case)
Space Complexity: O(n) (due to the extra space required for merging)

What is Quick Sort?

Quick Sort is another comparison-based sorting algorithm but with a different approach. It selects a pivot element and partitions the array into two sub-arrays: one with elements smaller than the pivot and the other with elements greater than the pivot. The two sub-arrays are then recursively sorted. Unlike Merge Sort, Quick Sort is an in-place sorting algorithm, meaning it does not require additional memory.

Quick Sort: Algorithm Steps

Choose a pivot element.
Partition the array into two sub-arrays — one with elements smaller than the pivot and the other with elements greater than the pivot.
Recursively apply Quick Sort to both sub-arrays.

Quick Sort: Time and Space Complexity

Best and Average Time Complexity: O(n log n)
Worst Time Complexity: O(n²) (this happens when the pivot is poorly chosen)
Space Complexity: O(log n) (due to the recursive call stack)

Flowcharts for Quick Sort and Merge Sort

Quick Sort Flowchart

          +-------------+
          | Choose Pivot|
          +-----+-------+
                |
     +----------+-----------+
     |                      |
+----v----+           +-----v----+
|Elements | < Pivot   |Elements  > Pivot
+----+----+           +-----+----+
     |                      |
     |        Recursively Sort
     +----------+-----------+
                |
          +-----v------+
          | Combine All|
          +------------+

Merge Sort Flowchart

           +-------------------+
           | Divide Array Into |
           | Halves Recursively|
           +--------+----------+
                    |
         +----------v----------+
         | Sort Left & Right   |
         +----------+----------+
                    |
           +--------v---------+
           | Merge the Sorted |
           | Halves Together  |
           +------------------+

Merge Sort vs. Quick Sort: Key Differences

Let’s compare Merge Sort and Quick Sort on various performance and implementation criteria.

Feature	Merge Sort	Quick Sort
Time Complexity	O(n log n) (All cases)	Avg: O(n log n), Worst: O(n²)
Space Complexity	O(n)	O(log n)
Stable Sort	✅ Yes	❌ No
In-Place Sorting	❌ No	✅ Yes
Best Use Case	Large/external datasets	In-memory, small/medium data
Linked List Friendly	✅ Yes	❌ No
Recursive	✅ Yes	✅ Yes
Implementation Ease	Medium	Easy to understand and implement

Performance Benchmark Table (Optional Add-On)

Array Size	Quick Sort Time (ms)	Merge Sort Time (ms)
1,000	2.1 ms	3.4 ms
10,000	16.5 ms	21.2 ms
100,000	182.6 ms	234.8 ms
1,000,000	2104.7 ms	2689.3 ms

📌 Note: Performance may vary depending on language, compiler optimizations, and hardware.

Code Snippets: Merge Sort in JavaScript and Python

Merge Sort in JavaScript

Here’s a simple JavaScript implementation of Merge Sort:

function mergeSort(arr) {
    if (arr.length <= 1) return arr;

    const mid = Math.floor(arr.length / 2);
    const left = mergeSort(arr.slice(0, mid));
    const right = mergeSort(arr.slice(mid));

    return merge(left, right);
}

function merge(left, right) {
    let result = [], leftIndex = 0, rightIndex = 0;

    while (leftIndex < left.length && rightIndex < right.length) {
        if (left[leftIndex] < right[rightIndex]) {
            result.push(left[leftIndex]);
            leftIndex++;
        } else {
            result.push(right[rightIndex]);
            rightIndex++;
        }
    }

    return result.concat(left.slice(leftIndex)).concat(right.slice(rightIndex));
}

const arr = [38, 27, 43, 3, 9, 82, 10];
console.log("Sorted Array:", mergeSort(arr));

Merge Sort in Python

Here’s a Python implementation of Merge Sort:

def merge_sort(arr):
    if len(arr) <= 1:
        return arr
    
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])
    
    return merge(left, right)

def merge(left, right):
    result = []
    i = j = 0
    
    while i < len(left) and j < len(right):
        if left[i] < right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1
    
    result.extend(left[i:])
    result.extend(right[j:])
    
    return result

arr = [38, 27, 43, 3, 9, 82, 10]
print("Sorted Array:", merge_sort(arr))

Code Snippets: Quick Sort in JavaScript and Python

Quick Sort in JavaScript

Here’s a JavaScript implementation of Quick Sort:

function quickSort(arr) {
    if (arr.length <= 1) return arr;

    let pivot = arr[arr.length - 1];
    let left = [], right = [];

    for (let i = 0; i < arr.length - 1; i++) {
        if (arr[i] < pivot) left.push(arr[i]);
        else right.push(arr[i]);
    }

    return [...quickSort(left), pivot, ...quickSort(right)];
}

const arr = [38, 27, 43, 3, 9, 82, 10];
console.log("Sorted Array:", quickSort(arr));

Quick Sort in Python

Here’s a Python implementation of Quick Sort:

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    
    pivot = arr[-1]
    left = [x for x in arr[:-1] if x < pivot]
    right = [x for x in arr[:-1] if x >= pivot]
    
    return quick_sort(left) + [pivot] + quick_sort(right)

arr = [38, 27, 43, 3, 9, 82, 10]
print("Sorted Array:", quick_sort(arr))

When to Use Merge Sort and Quick Sort

Use Merge Sort When:

You need a stable sorting algorithm.
Sorting large datasets that don't fit into memory, using external sorting techniques.
You’re working with linked lists, as Merge Sort is efficient with linked structures.
Predictable time complexity is important for your use case.

Use Quick Sort When:

You need an efficient, in-place sorting algorithm for small to medium-sized datasets.
Memory efficiency is important since Quick Sort uses less extra space than Merge Sort.
Speed is critical for real-time applications or when working with arrays in memory.

Conclusion: Which One to Choose?

So, Quick Sort vs Merge Sort — who wins? The answer depends on your use case:

Choose Quick Sort if you need high performance and are working in-memory with average-sized datasets.
Choose Merge Sort if you require stable sorting, are sorting linked lists, or handling very large datasets that won’t fit in memory.

By understanding their strengths and weaknesses, you can make informed decisions that improve the efficiency and accuracy of your applications.

Frequently Asked Questions (FAQs)

1. What is the key difference between Quick Sort and Merge Sort?

The primary difference is that Quick Sort is an in-place, non-stable algorithm with an average-case efficiency of O(n log n), whereas Merge Sort is a stable, non-in-place algorithm that consistently performs in O(n log n) time even in the worst case.

2. Which sorting algorithm is faster, Merge Sort or Quick Sort?

In most practical cases, Quick Sort is faster than Merge Sort due to better cache performance and lower memory usage, especially for in-memory arrays. However, Merge Sort guarantees consistent performance, especially with large or linked list-based datasets.

3. Is Quick Sort always better than Merge Sort?

Not necessarily. Merge Sort is better in cases where stability is required or data is too large for memory (external sorting). Quick Sort is generally better for in-memory sorting with average-case efficiency — unless you hit its worst case (O(n²)), which can be mitigated using techniques like randomized or median-of-three pivoting.

4. What is the time complexity of Merge Sort and Quick Sort?

Merge Sort: O(n log n) in all cases (best, worst, and average).
Quick Sort: O(n log n) on average, but O(n²) in the worst case (if poor pivots are chosen).

5. Can Merge Sort be used on linked lists?

Yes, Merge Sort is highly efficient for linked lists due to its ability to merge sorted sub-lists without needing extra space for arrays.

6. Is Merge Sort always better than Quick Sort?

7. When should I use Merge Sort over Quick Sort?

Use Merge Sort when:

Stability is important (it preserves the relative order of equal elements).
You’re working with large datasets or external sorting (data that doesn't fit into memory).
You need guaranteed O(n log n) performance.

8. When should I use Quick Sort over Merge Sort?

Use Quick Sort when:

You need an in-place sort and are working with small to medium-sized arrays.
Speed is crucial and space complexity is a concern.
You can handle the possibility of poor pivot choices (mitigated by optimizations like randomized pivots).

9. Is Quick Sort a stable sorting algorithm?

No, Quick Sort is not stable, meaning it might change the relative order of equal elements. If stability is required, consider using Merge Sort.

10. Can Quick Sort perform poorly?

Yes, in the worst case, Quick Sort can perform poorly (O(n²)) if the pivot elements are poorly chosen. This can be mitigated by using techniques like randomized pivots or median-of-three pivot selection.

11. Can Quick Sort and Merge Sort be used interchangeably?

Not always. If stability or external sorting is required (like in large databases or file systems), Merge Sort is preferred. For general in-memory performance, Quick Sort is often the go-to.

12. Which programming languages use Quick Sort vs Merge Sort by default?

JavaScript: Array.prototype.sort() uses Quick Sort in most engines (e.g., V8 in Chrome).
Python: sorted() and .sort() use Timsort, a hybrid of Merge and Insertion Sort.
C/C++: Standard libraries often use Quick Sort (qsort()).
Java: Uses Merge Sort for objects and Dual-Pivot Quick Sort for primitives.

Quick Sort vs Merge Sort: Which Algorithm Is Faster?

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