Efficiently Sorting Linked Lists Using Merge Sort

According to a recent report by NASSCOM, India's data analytics industry is projected to reach $16 billion by 2025, growing at a CAGR of 26%. This rapid expansion highlights the increasing reliance on data mining software to process, analyze, and extract meaningful insights from vast datasets. 

Companies in finance and healthcare use advanced tools to improve decision-making, enhance customer experiences, and detect fraud. As businesses generate more data than ever, the demand for efficient data mining solutions is set to rise. This blog explores Merge Sort for Linked Lists, its applications, and real-world use cases.

Understanding Merge Sort for Linked Lists

Merge Sort is an efficient sorting algorithm for linked lists, using a divide-and-conquer approach. It splits the list into halves, recursively sorts them, and merges them back in sorted order. Unlike arrays, Merge Sort is preferred for linked lists as it doesn’t require random access. It runs in O(n log n) time and ensures stable sorting.

The algorithm works by repeatedly dividing the list until single-node lists remain, then merging them in order. This makes it ideal for large linked lists, ensuring efficient and stable sorting with minimal extra memory.

Let’s explore how Merge Sort works for Linked Lists in detail.

How does Merge Sort Work for Linked Lists?

Merge Sort for Linked Lists follows a divide-and-conquer approach. It breaks the list into smaller parts, sorts them, and merges them back together. This method is efficient for linked lists because it doesn’t require extra space for shifting elements like arrays.

Here are the key steps involved in Merge Sort for Linked Lists:

• Splitting the List: The linked list is divided into two halves using the slow and fast pointer technique, commonly used in platforms like LeetCode and GeeksforGeeks.
• Sorting Sublists: Each half is recursively sorted using Merge Sort, which ensures stability in data structures used in the fintech and healthcare industries.
• Merging Sublists: The sorted halves are merged using a two-pointer technique, widely used in search engines and cloud databases like Google BigQuery.

Now that you understand the process let’s move on to the key properties of Merge Sort.

Key Properties of Merge Sort

Merge Sort for Linked Lists has several fundamental properties, making it a preferred sorting algorithm for linked list-based operations.

Below are the key characteristics:

• Divide-and-conquer strategy: This technique recursively breaks down a problem into smaller subproblems and is widely used in AI-driven applications.
• Recursion-Based Approach: The algorithm continuously calls itself until base conditions are met, ensuring efficiency in data analytics tools like Pandas and NumPy.
• Stability and Order Preservation: It maintains the relative order of equal elements, making it suitable for databases in stock market trading and customer relationship management (CRM) software.

Also Read: Sorting in Data Structure: Categories & Types [With Examples]

With these properties in mind, let’s explore How to implement merge sort for linked lists.

How to Implement Merge Sort for Linked Lists?

You need to use a recursive approach to implement merge sort for linked lists. The algorithm breaks the list into smaller sublists, sorts them individually, and then merges them in a sorted order. Unlike array-based sorting algorithms, this technique is handy for linked lists since it does not require additional space for element shifting. 

Want to boost your resume with real-world data projects? upGrad’s Tableau, Python & SQL course offers hands-on experience. Earn a certificate from a top university!

Let's explore how to implement merge sort for linked lists in Python.

How do you implement merge sort for linked lists in Python?

In Python, implementing Merge Sort for Linked Lists relies on recursion. The key steps include:

• Finding the middle of the list using the slow and fast pointer technique.
• Recursively sorting each half of the split list.
• Merging the sorted halves using a helper function.

Python Implementation:

Below is a Python implementation of Merge Sort for Linked Lists. The example sorts a singly linked list in ascending order.

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

# Function to merge two sorted linked lists
def merge_sorted_lists(l1, l2):
    dummy = ListNode()
    tail = dummy

    while l1 and l2:
        if l1.val < l2.val:
            tail.next, l1 = l1, l1.next
        else:
            tail.next, l2 = l2, l2.next
        tail = tail.next

    tail.next = l1 or l2
    return dummy.next

# Function to find the middle of the linked list
def find_middle(head):
    slow, fast = head, head
    prev = None

    while fast and fast.next:
        prev, slow, fast = slow, slow.next, fast.next.next

    if prev:
        prev.next = None  # Splitting the list

    return slow

# Merge Sort function
def merge_sort(head):
    if not head or not head.next:
        return head

    mid = find_middle(head)
    left = merge_sort(head)
    right = merge_sort(mid)

    return merge_sorted_lists(left, right)

# Function to print linked list
def print_list(head):
    while head:
        print(head.val, end=" -> ")
        head = head.next
    print("None")

# Example Usage
head = ListNode(4, ListNode(2, ListNode(1, ListNode(3))))
sorted_head = merge_sort(head)
print_list(sorted_head)

Output:

1 -> 2 -> 3 -> 4 -> None

Explanation:

1. ListNode Class: Defines a node of a singly linked list.
2. merge_sorted_lists(): Merges two sorted linked lists into one.
3. find_middle(): Uses slow and fast pointers to find the middle node and split the list.
4. merge_sort(): Recursively sorts and merges the left and right halves.
5. print_list(): Prints the linked list for verification.

Let's explore how to implement merge sort for linked lists in C++.

How to Implement Merge Sort for Linked Lists in C++?

The C++ implementation of Merge Sort for linked lists follows the same logic as Python but differs in syntax and memory management. The core steps remain:

• Splitting the list using the two-pointer technique.
• Sorting the halves recursively using Merge Sort.
• Merging the sorted sublists using a helper function.

C++ Implementation

Here’s a C++ implementation of Merge Sort for Linked Lists:

#include <iostream>
using namespace std;

struct ListNode {
    int val;
    ListNode* next;
    ListNode(int x) : val(x), next(NULL) {}
};

// Function to merge two sorted linked lists
ListNode* merge_sorted_lists(ListNode* l1, ListNode* l2) {
    ListNode dummy(0);
    ListNode* tail = &dummy;

    while (l1 && l2) {
        if (l1->val < l2->val) {
            tail->next = l1;
            l1 = l1->next;
        } else {
            tail->next = l2;
            l2 = l2->next;
        }
        tail = tail->next;
    }

    tail->next = l1 ? l1 : l2;
    return dummy.next;
}

// Function to find the middle of the linked list
ListNode* find_middle(ListNode* head) {
    if (!head || !head->next) return head;

    ListNode *slow = head, *fast = head, *prev = NULL;
    while (fast && fast->next) {
        prev = slow;
        slow = slow->next;
        fast = fast->next->next;
    }

    if (prev) prev->next = NULL; // Splitting the list

    return slow;
}

// Merge Sort function
ListNode* merge_sort(ListNode* head) {
    if (!head || !head->next) return head;

    ListNode* mid = find_middle(head);
    ListNode* left = merge_sort(head);
    ListNode* right = merge_sort(mid);

    return merge_sorted_lists(left, right);
}

// Function to print linked list
void print_list(ListNode* head) {
    while (head) {
        cout << head->val << " -> ";
        head = head->next;
    }
    cout << "NULL" << endl;
}

// Example Usage
int main() {
    ListNode* head = new ListNode(4);
    head->next = new ListNode(2);
    head->next->next = new ListNode(1);
    head->next->next->next = new ListNode(3);

    head = merge_sort(head);
    print_list(head);

    return 0;
}

Output

1 -> 2 -> 3 -> 4 -> NULL

Explanation:

1. ListNode Struct: Defines a node in the linked list.
2. merge_sorted_lists(): Merges two sorted linked lists.
3. find_middle(): Uses slow and fast pointers to find and split the middle node.
4. merge_sort(): Recursively sorts the linked list and merges sorted halves.
5. print_list(): Prints the linked list to verify sorting.

Also Read: 12 Essential Features of C++: Understanding Its Strengths and Challenges in 2025

Python and C++ implementations follow the same logical structure but differ in syntax and memory management. Now, let's analyze merge sort's time and space complexity for linked lists.

Time Complexity & Space Complexity of Merge Sort for Linked Lists

Merge Sort for Linked Lists is an efficient sorting algorithm with a time complexity of O(n log n), making it suitable for large datasets. Unlike quicksort, which relies on partitioning, merge sort divides the list into halves, sorts them recursively, and merges them in sorted order. The space complexity varies based on whether a recursive or iterative approach is used.

Below are the key aspects of time and space complexity in Merge Sort for Linked Lists:

• Time Complexity - O(n log n): Since the list is repeatedly divided into two halves and merged, the algorithm runs in O(n log n) time, making it efficient for processing large datasets in fintech, cloud computing, and e-commerce.
• Space Complexity in Recursive Implementation—O (log n): The recursion stack requires extra memory, leading to O(log n) space usage, which is commonly seen in AI-driven analytics tools like TensorFlow and Scikit-learn.
• Space Complexity in Iterative Implementation - O(1): An iterative version of merge sort avoids recursive calls, reducing space usage, which is beneficial for memory-intensive applications like blockchain and cybersecurity.
• Comparison with Other Sorting Algorithms—Compared to quicksort, which has an average-case complexity of O(n log n) but a worst-case complexity of O(n²), merge sort offers consistent performance, making it ideal for linked lists in stock trading and CRM platforms.

Excel in coding challenges with upGrad’s Data Structures & Algorithms Course and take your career in data to the next level.

Now that you understand the complexities, let's explore the practical applications of merge sorting for linked lists.

Practical Applications of Merge Sort for Linked Lists

Merge Sort for Linked Lists is widely used in real-world applications due to its efficiency in handling large datasets and maintaining stability. It is particularly beneficial when linked lists are preferred over arrays, such as dynamic memory allocation, large-scale data processing, and database management.

Below are some key practical applications of Merge Sort for Linked Lists:

• Database Management Systems: Used in indexing and sorting records efficiently in databases like MySQL and PostgreSQL, where linked lists help manage memory dynamically.
• File Sorting in Operating Systems is ideal for sorting large files stored as linked lists in file systems like HDFS (Hadoop Distributed File System), which is commonly used in big data applications.
• Network Traffic Analysis: Helps sort large volumes of network packets in cybersecurity tools like Wireshark to detect unusual patterns and anomalies.
• Stock Market Data Processing: Used in algorithmic trading platforms to sort and analyze stock transactions stored as linked lists for real-time decision-making.
• Task Scheduling in Cloud Computing helps distribute workloads and allocate resources in cloud environments like AWS Lambda and Microsoft Azure.

Considering these applications, let’s explore the Advantages and disadvantages of Merge Sorting for Linked Lists.

Also Read: What is a Database Management System? Tools, Techniques and Optimization

Advantages and Limitations of Merge Sort for Linked Lists

Like any other algorithm, Merge Sort has its limitations. While it performs well for linked lists, its recursive nature can sometimes increase space complexity.

Below is a detailed comparison of the advantages and limitations of Merge Sort for Linked Lists:

Also Read: Python Program for Merge Sort

Understanding these advantages and limitations will help you decide when to use Merge Sort for Linked Lists. Now, let’s explore the best practices for implementing merge sort on linked lists.

Best Practices for Implementing Merge Sort on Linked Lists

Implementing Merge Sort for Linked Lists efficiently requires careful handling of recursion, memory management, and optimization techniques. Writing clean and optimized code can improve performance, reduce unnecessary operations, and make debugging easier.

Below are some best practices for implementing Merge Sort on Linked Lists:

• Use the Fast and Slow Pointer Technique for Splitting: Instead of iterating through the list to find the middle element, use the fast and slow pointer approach, which is commonly applied in competitive programming and coding platforms like LeetCode.
• Optimize Recursion to Prevent Stack Overflow: Avoid excessive recursive calls by implementing an iterative merge step where possible, which is beneficial in large-scale data processing applications like Hadoop and Spark.
• Minimize Auxiliary Space Usage: To reduce memory overhead, use in-place merging instead of creating new linked lists during the merging process, especially in embedded systems and IoT applications.

• Handle Edge Cases Properly: Ensure the code handles empty lists, single-node lists, and already sorted lists efficiently. 

  For instance, merge sort should return an empty or single-node list as is and avoid unnecessary operations on sorted lists. These edge cases are common in CRM and e-commerce systems.

• Use Tail Recursion Optimization: Some programming languages support tail recursion optimization to improve performance. This is particularly useful in functional programming paradigms like Haskell and Scala.
• Avoid Excessive Pointer Reassignments: Reassigning too many pointers can lead to inefficiencies. Using a dummy node simplifies merging and improves readability, making debugging easier in AI-based applications like TensorFlow and Scikit-learn.

By following these best practices, you can implement Merge Sort for Linked Lists effectively. 

Also Read: Complete Guide to the Merge Sort Algorithm: Features, Working, and More

How Can upGrad Help You Learn Merge Sort for Linked Lists?

Understanding Merge Sort for Linked Lists is challenging but essential for technical interviews and mastering DSA. upGrad’s programs provide hands-on projects and expert-led courses to help you grasp sorting algorithms, linked lists, and advanced DSA concepts effectively.

Below are some of upGrad’s top programs that can help you learn Merge Sort for Linked Lists:

• Learn Basic Python Programming
• Professional Certificate Program in AI and Data Science
• Learn Basic Python Programming
• Programming with Python: Introduction for Beginners

Are you finding it difficult to decide which program suits your career goals? Speak to an upGrad career counselor for personalized guidance. You can also visit an upGrad offline centre near you to explore learning opportunities and career advancement options.

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

Top Data Science Skills to Learn

Stay informed and inspired with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!

Read our popular Data Science Articles

Reference Links:
https://ijrpr.com/uploads/V4ISSUE5/IJRPR12935.pdf
https://www.analytixlabs.co.in/blog/the-present-and-the-future-of-big-data-in-india/
https://https://www.appliedaicourse.com/blog/data-analyst-salary-in-india/
www.ibef.org/blogs/scope-of-data-analytics-in-india-and-future