Data Structures & Algorithms
Table of content:
- What Is Data Structure?
- Key Features Of Data Structures
- Basic Terminologies Related To Data Structures
- Types Of Data Structures
- What Are Linear Data Structures & Its Types?
- What Are Non-Linear Data Structures & Its Types?
- Importance Of Data Structure In Programming
- Basic Operations On Data Structures
- Applications Of Data Structures
- Real-Life Applications Of Data Structures
- Linear Vs. Non-linear Data Structures
- What Are Algorithms? The Difference Between Data Structures & Algorithms
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Asymptotic Notation?
- How Asymptotic Notation Helps In Analyzing Performance
- Types Of Asymptotic Notation
- Big-O Notation (O)
- Omega Notation (Ω)
- Theta Notation (Θ)
- Little-O Notation (o)
- Little-Omega Notation (ω)
- Summary Of Asymptotic Notations
- Real-World Applications Of Asymptotic Notation
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding Big O Notation
- Types Of Time Complexity
- Space Complexity In Big O Notation
- How To Determine Big O Complexity
- Best, Worst, And Average Case Complexity
- Applications Of Big O Notation
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Time Complexity?
- Why Do You Need To Calculate Time Complexity?
- The Time Complexity Algorithm Cases
- Time Complexity: Different Types Of Asymptotic Notations
- How To Calculate Time Complexity?
- Time Complexity Of Sorting Algorithms
- Time Complexity Of Searching Algorithms
- Writing And optimizing An algorithm
- What Is Space Complexity And Its Significance?
- Relation Between Time And Space Complexity
- Conclusion
Table of content:
- What Is Linear Data Structure?
- Key Characteristics Of Linear Data Structures
- What Are The Types Of Linear Data Structures?
- What Is An Array Linear Data Structure?
- What Are Linked Lists Linear Data Structure?
- What Is A Stack Linear Data Structure?
- What Is A Queue Linear Data Structure?
- Most Common Operations Performed in Linear Data Structures
- Advantages Of Linear Data Structures
- What Is Nonlinear Data Structure?
- Difference Between Linear & Non-Linear Data Structures
- Who Uses Linear Data Structures?
- Conclusion
- Frequently Asked Questions
Table of content:
- What is a linear data structure?
- What is a non-linear data structure?
- Difference between linear data structure and non-linear data structure
- FAQs about linear and non-linear data structures
Table of content:
- What Is Sorting In Data Structures?
- What Is Bubble Sort?
- What Is Selection Sort?
- What Is Insertion Sort?
- What Is Merge Sort?
- What Is Quick Sort?
- What Is Heap Sort?
- What Is Radix Sort?
- What Is Bucket Sort?
- What Is Counting Sort?
- What Is Shell Sort?
- Choosing The Right Sorting Algorithm
- Applications Of Sorting
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding Bubble Sort Algorithm
- Bubble Sort Algorithm
- Implementation Of Bubble Sort In C++
- Time And Space Complexity Analysis Of Bubble Sort Algorithm
- Advantages Of Bubble Sort Algorithm
- Disadvantages Of Bubble Sort Algorithm
- Applications of Bubble Sort Algorithms
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding The Merge Sort Algorithm
- Algorithm For Merge Sort
- Implementation Of Merge Sort In C++
- Time And Space Complexity Analysis Of Merge Sort
- Advantages And Disadvantages Of Merge Sort
- Applications Of Merge Sort
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding The Selection Sort Algorithm
- Algorithmic Approach To Selection Sort
- Implementation Of Selection Sort Algorithm
- Complexity Analysis Of Selection Sort
- Comparison Of Selection Sort With Other Sorting Algorithms
- Advantages And Disadvantages Of Selection Sort
- Application Of Selection Sort
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Insertion Sort Algorithm?
- How Does Insertion Sort Work? (Step-by-Step Explanation)
- Implementation of Insertion Sort in C++
- Time And Space Complexity Of Insertion Sort
- Applications Of Insertion Sort Algorithm
- Comparison With Other Sorting Algorithms
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding Quick Sort Algorithm
- Step-By-Step Working Of Quick Sort
- Implementation Of Quick Sort Algorithm
- Time Complexity Analysis Of Quick Sort
- Advantages Of Quick Sort Algorithm
- Disadvantages Of Quick Sort Algorithm
- Applications Of Quick Sort Algorithm
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding The Heap Data Structure
- Working Of Heap Sort Algorithm
- Implementation Of Heap Sort Algorithm
- Advantages Of Heap Sort
- Disadvantages Of Heap Sort
- Real-World Applications Of Heap Sort
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding The Counting Sort Algorithm
- Conditions For Using Counting Sort Algorithm
- How Counting Sort Algorithm Works?
- Implementation Of Counting Sort Algorithm
- Time And Space Complexity Analysis Of Counting Sort
- Comparison Of Counting Sort With Other Sorting Algorithms
- Advantages Of Counting Sort Algorithm
- Disadvantages Of Counting Sort Algorithm
- Applications Of Counting Sort Algorithm
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding Shell Sort Algorithm
- Working Of Shell Sort Algorithm
- Implementation Of Shell Sort Algorithm
- Time Complexity Analysis Of Shell Sort Algorithm
- Advantages Of Shell Sort Algorithm
- Disadvantages Of Shell Sort Algorithm
- Applications Of Shell Sort Algorithm
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is The Binary Search Algorithm?
- Conditions For Using Binary Search
- Steps For Implementing Binary Search
- Iterative Method For Binary Search With Implementation Examples
- Recursive Method For Binary Search
- Complexity Analysis Of Binary Search Algorithm
- Iterative Vs. Recursive Implementation Of Binary Search
- Advantages & Disadvantages Of Binary Search
- Practical Applications & Real-World Examples Of Binary Search
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Linked List In Data Structures?
- Types Of Linked Lists In Data Structures
- Linked List Operations In Data Structures
- Advantages And Disadvantages Of Linked Lists In Data Structures
- Comparison Of Linked Lists And Arrays
- Applications Of Linked Lists
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is A Singly Linked List In Data Structure?
- Insertion Operations On Singly Linked Lists
- Deletion Operation On Singly Linked List
- Searching For Elements In Single Linked List
- Calculating Length Of Single Linked List
- Practical Applications Of Singly Linked Lists In Data Structure
- Common Problems With Singly Linked Lists
- Conclusion
- Frequently Asked Questions (FAQ)
Table of content:
- What Is A Linked List?
- Reverse A Linked List
- How To Reverse A Linked List? (Approaches)
- Recursive Approach To Reverse A Linked List
- Iterative Approach To Reverse A Linked List
- Using Stack To Reverse A Linked List
- Complexity Analysis Of Different Approaches To Reverse A Linked List
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is A Graph Data Structure?
- Importance Of Graph Data Structures
- Types Of Graphs In Data Structure
- Types Of Graph Algorithms
- Application Of Graphs In Data Structures
- Challenges And Complexities In Graphs
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Tree Data Structure?
- Terminologies Of Tree Data Structure:
- Different Types Of Tree Data Structures
- Basic Operations On Tree Data Structure
- Applications Of Tree Data Structures
- Comparison Of Trees, Graphs, And Linear Data Structures
- Advantages Of Tree Data Structure
- Disadvantages Of Tree Data Structure
- Conclusion
- Frequently Asked Questions
Table of content:
- What Is Dynamic Programming?
- Real-Life Example: The Jigsaw Puzzle Analogy
- How To Solve A Problem Using Dynamic Programming?
- Dynamic Programming Algorithm Techniques
- Advantages Of Dynamic Programming
- Disadvantages Of Dynamic Programming
- Applications Of Dynamic Programming
- Conclusion
- Frequently Asked Questions
Table of content:
- Understanding The Sliding Window Algorithm
- How Does The Sliding Window Algorithm Works?
- How To Identify Sliding Window Problems?
- Fixed-Size Sliding Window Example: Maximum Sum Subarray Of Size k
- Variable-Size Sliding Window Example: Smallest Subarray With A Given Sum
- Advantages Of Sliding Window Technique
- Disadvantages Of Sliding Window Technique
- Conclusion
- Frequently Asked Questions
Table of content:
- Introduction To Data Structures
- Data Structures Interview Questions: Basics
- Data Structures Interview Questions: Intermediate
- Data Structures Interview Questions: Advanced
- Conclusion
Linked List In Data Structures | Types, Operations & More (+Code)

In data structures, a linked list is a dynamic linear data structure where elements, called nodes, are connected using pointers. Unlike arrays, linked lists do not require contiguous memory allocation, making them efficient for insertions and deletions. Each node typically consists of data and a pointer to the next node, forming a chain-like structure.
In this article, we will explore the types of linked lists, their implementation, advantages, and real-world applications to understand why they are a fundamental concept in data structures.
What Is Linked List In Data Structures?
A Linked List is a data structure used to store a collection of elements, where each element (called a node) contains two parts:
- Data – The actual value stored in the node.
- Pointer – A reference (or link) to the next node in the sequence.
Unlike arrays, linked lists do not store elements in a continuous memory location. Instead, each node is connected to the next through pointers, making insertion and deletion operations more efficient compared to arrays.
Real-Life Analogy: A Train
Think of a linked list as a train where:
- Each coach represents a node (it has data: passengers, and a link to the next coach).
- The engine (head of the linked list) points to the first coach.
- If we want to add or remove a coach, we don’t need to shift all other coaches (unlike an array where shifting might be required).
- The last coach (node) has no further link, just like the tail of a linked list points to NULL.
Types Of Linked Lists In Data Structures
A linked list is a dynamic data structure consisting of nodes connected through pointers. Depending on how nodes are linked, linked lists can be classified into different types.
1. Singly Linked List
A singly linked list consists of nodes where each node contains:
- Data (the value stored in the node).
- A pointer to the next node in the sequence.
This type of linked list allows only forward traversal from the head (first node) to the tail (last node),which points to NULL.
Structure:
[10 | * ] → [20 | * ] → [30 | * ] → [NULL]
Real-Life Analogy:
A one-way street where vehicles move in a single direction without the ability to turn back.
Advantages:
- Uses less memory as each node stores only one pointer.
- Insertion and deletion at the beginning are efficient.
Disadvantages:
- Cannot traverse backward due to the absence of a previous pointer.
- Searching requires a full scan from the head node.
2. Doubly Linked List
A doubly linked list is an extension of a singly linked list where each node contains:
- Data
- A pointer to the next node
- A pointer to the previous node
This structure allows both forward and backward traversal.
Structure:
NULL ← [10 | * | * ] ↔ [20 | * | * ] ↔ [30 | * | * ] → NULL
Real-Life Analogy:
A metro train where passengers can move forward or backward between compartments.
Advantages:
- Bidirectional traversal allows movement in both directions.
- Easier deletion of a node without requiring a full traversal.
Disadvantages:
- Requires more memory due to two pointers per node.
- Slightly slower operations due to additional pointer management.
3. Circular Linked List
A circular linked list is a variation where the last node points back to the first node, forming a continuous loop. It can be either singly circular or doubly circular.
Structure (Singly Circular):
[10 | * ] → [20 | * ] → [30 | * ] → (points back to 10)
Structure (Doubly Circular):
[10 | * | * ] ↔ [20 | * | * ] ↔ [30 | * | * ] ↺ (points back to 10)
Real-Life Analogy:
A circular race track where cars keep moving in a loop without a definite end.
Advantages:
- No NULL values, enabling continuous traversal.
- Useful in applications like round-robin scheduling and buffered tasks.
Disadvantages:
- Can lead to infinite loops if not properly handled.
- More complex implementation compared to singly and doubly linked lists.
Choosing The Right Linked List
- Singly Linked List is suitable when memory optimization is a priority and one-way traversal is sufficient.
- Doubly Linked List is useful when frequent insertions and deletions are required, and backward traversal is needed.
- Circular Linked List is ideal when a continuous looping structure is required, such as in operating systems and scheduling algorithms.
Linked List Operations In Data Structures
A linked list is a dynamic data structure that allows insertion and deletion of elements efficiently. The fundamental operations on a linked list include:
1. Insertion
Adding a new node at different positions in the linked list.
Types of Insertion:
- At the Beginning: The new node becomes the head, and its pointer is set to the previous head.
- At a Specific Position: The new node is inserted between two existing nodes, adjusting their pointers.
- At the End: The new node is added at the tail, and the last node’s pointer is updated.
Real-Life Analogy:
Adding a new coach to a train at the front, middle, or end of a railway track.
Complexity:
- Best Case: O(1) (insertion at the beginning)
- Worst Case: O(n) (insertion at the end or middle)
Code Example:
#include <iostream>
using namespace std;
class Node {
public:
int data;
Node* next;
Node(int value) {
data = value;
next = NULL;
}
};
// Function to insert at the beginning
void insertAtBeginning(Node*& head, int value) {
Node* newNode = new Node(value);
newNode->next = head;
head = newNode;
}
// Function to insert at the end
void insertAtEnd(Node*& head, int value) {
Node* newNode = new Node(value);
if (head == NULL) {
head = newNode;
return;
}
Node* temp = head;
while (temp->next != NULL) {
temp = temp->next;
}
temp->next = newNode;
}
// Function to insert at a specific position
void insertAtPosition(Node*& head, int value, int position) {
if (position == 0) {
insertAtBeginning(head, value);
return;
}
Node* newNode = new Node(value);
Node* temp = head;
for (int i = 0; temp != NULL && i < position - 1; i++) {
temp = temp->next;
}
if (temp == NULL) {
cout << "Position out of bounds!" << endl;
return;
}
newNode->next = temp->next;
temp->next = newNode;
}
// Function to print the list
void printList(Node* head) {
while (head != NULL) {
cout << head->data << " -> ";
head = head->next;
}
cout << "NULL" << endl;
}
int main() {
Node* head = NULL;
insertAtBeginning(head, 10);
insertAtEnd(head, 30);
insertAtPosition(head, 20, 1);
printList(head); // Output: 10 -> 20 -> 30 -> NULL
return 0;
}
Output:
10 -> 20 -> 30 -> NULL
Explanation:
In the above code example-
- Node Class:
- We define a Node class with data (to store values) and next (to point to the next node).
- The constructor initializes the data and sets next to NULL.
- Insert at Beginning:
- We create a new node and point its next to the current head.
- The head is updated to the new node.
- Insert at End:
- If the list is empty, the new node becomes the head.
- Otherwise, we traverse to the last node and update its next to the new node.
- Insert at Position:
- If inserting at position 0, we call insertAtBeginning().
- Otherwise, we traverse to the given position and adjust pointers.
- If the position is out of bounds, we display an error message.
- Print Function:
- We traverse the list and print each node’s value.
- Main Function:
- We start with an empty list.
- Insert 10 at the beginning, 30 at the end, and 20 at position 1.
- Finally, we print the list: 10 -> 20 -> 30 -> NULL
2. Deletion
Removing a node from the linked list and updating pointers accordingly.
Types of Deletion:
- From the Beginning: The head node is removed, and the next node becomes the new head.
- From a Specific Position: The node is removed, and the previous node’s pointer is updated to skip it.
- From the End: The last node is removed, and the second-last node’s pointer is set to NULL.
Real-Life Analogy:
Removing a specific coach from a train without affecting the remaining coaches.
Complexity:
- Best Case: O(1) (deleting the first node)
- Worst Case: O(n) (deleting the last node)
Code Example:
void deleteNode(Node*& head, int position) {
if (head == NULL) {
cout << "List is empty!" << endl;
return;
}
Node* temp = head;
// Deleting the first node
if (position == 0) {
head = head->next;
delete temp;
return;
}
// Find the previous node
for (int i = 0; temp != NULL && i < position - 1; i++) {
temp = temp->next;
}
if (temp == NULL || temp->next == NULL) {
cout << "Position out of bounds!" << endl;
return;
}
Node* nodeToDelete = temp->next;
temp->next = nodeToDelete->next;
delete nodeToDelete;
}
int main() {
Node* head = NULL;
insertAtEnd(head, 10);
insertAtEnd(head, 20);
insertAtEnd(head, 30);
cout << "Before deletion: ";
printList(head);
deleteNode(head, 1);
cout << "After deletion: ";
printList(head);
return 0;
}
Output:
Before deletion: 10 -> 20 -> 30 -> NULL
After deletion: 10 -> 30 -> NULL
Explanation:
In the above code example-
- Delete Node Function:
- If the list is empty, we print "List is empty!" and return.
- If deleting the first node, we update head to the next node and free the memory.
- Otherwise, we traverse to the node before the target position.
- If the position is out of bounds, we print an error message.
- Otherwise, we adjust pointers and delete the node.
- Main Function:
- We start with an empty list.
- Insert 10, 20, and 30 at the end.
- Print the list before deletion.
- Delete the node at position 1 (removing 20).
- Print the list after deletion.
3. Traversal
Visiting each node in the linked list to access data.
Approach:
- Start from the head node.
- Move to the next node using the pointer until reaching NULL.
Real-Life Analogy:
Walking from the first to the last coach of a train, checking each passenger.
Complexity:
- Time Complexity: O(n) (as every node needs to be visited)
Code Example:
void traverseList(Node* head) {
Node* temp = head;
while (temp != NULL) {
cout << temp->data << " ";
temp = temp->next;
}
cout << endl;
}
int main() {
Node* head = NULL;
insertAtEnd(head, 10);
insertAtEnd(head, 20);
insertAtEnd(head, 30);
cout << "Linked List: ";
traverseList(head);
return 0;
}
Output:
Linked List: 10 20 30
Explanation:
In the above code example-
- Traverse List Function:
- We start from head and iterate through the list.
- For each node, we print its data.
- The loop stops when temp becomes NULL.
- Main Function:
- We initialize an empty list.
- Insert 10, 20, and 30 at the end.
- Print the list using traverseList().
4. Searching
Finding a specific node based on its value.
Approach:
- Start from the head.
- Compare each node’s value with the target value.
- Continue until the value is found or the end is reached.
Real-Life Analogy:
Searching for a specific seat number inside a train by checking each seat.
Complexity:
- Worst Case: O(n) (linear search)
Code Example:
bool search(Node* head, int key) {
Node* temp = head;
while (temp != NULL) {
if (temp->data == key)
return true;
temp = temp->next;
}
return false;
}
int main() {
Node* head = NULL;
insertAtEnd(head, 10);
insertAtEnd(head, 20);
insertAtEnd(head, 30);
int key = 20;
if (search(head, key))
cout << key << " found in the list!" << endl;
else
cout << key << " not found in the list!" << endl;
return 0;
}
Output:
20 found in the list!
Explanation:
In the above code example-
- Search Function:
- We start from head and iterate through the list.
- If a node’s data matches the key, we return true.
- If we reach the end without finding the key, we return false.
- Main Function:
- We initialize an empty list.
- Insert 10, 20, and 30 at the end.
- Search for 20 in the list.
- Print whether the key is found or not.
5. Updating
Changing the data of an existing node.
Approach:
- Traverse to the required node.
- Modify its value without changing the structure.
Real-Life Analogy:
Updating a passenger's ticket information while they remain in the same seat.
Complexity:
- Worst Case: O(n) (if the last node needs to be updated)
Code Example:
void updateNode(Node* head, int oldValue, int newValue) {
Node* temp = head;
while (temp != NULL) {
if (temp->data == oldValue) {
temp->data = newValue;
return;
}
temp = temp->next;
}
cout << "Value not found!" << endl;
}
int main() {
Node* head = NULL;
insertAtEnd(head, 10);
insertAtEnd(head, 20);
insertAtEnd(head, 30);
cout << "Before update: ";
printList(head);
updateNode(head, 20, 25);
cout << "After update: ";
printList(head);
return 0;
}
Output:
Before update: 10 -> 20 -> 30 -> NULL
After update: 10 -> 25 -> 30 -> NULL
Explanation:
In the above code example-
- Update Node Function:
- We start from head and iterate through the list.
- If a node’s data matches oldValue, we update it to newValue and return.
- If the value is not found, we print "Value not found!".
- Main Function:
- We initialize an empty list.
- Insert 10, 20, and 30 at the end.
- Print the list before the update.
- Update 20 to 25.
- Print the list after the update.
Choosing The Right Operation Based On Need
- Use insertion when adding new elements dynamically.
- Use deletion to remove unnecessary elements.
- Use traversal to access and process all elements.
- Use searching to locate a specific node.
- Use updating when modifying values without altering structure.
Summary Of Operations & Their Complexities
Operation |
Best Case |
Worst Case |
Insertion |
O(1) (At Beginning) |
O(n) (At End) |
Deletion |
O(1) (First Node) |
O(n) (Last Node) |
Traversal |
O(n) |
O(n) |
Searching |
O(1) (First Node) |
O(n) |
Updating |
O(1) (First Node) |
O(n) |
Advantages And Disadvantages Of Linked Lists In Data Structures
Linked lists provide dynamic memory allocation and efficient insertions/deletions but come with increased memory overhead. Let's explore the key advantages and disadvantages:
Advantages Of Linked Lists
- Dynamic Memory Allocation
- Unlike arrays, linked lists do not require a fixed size during declaration.
- Memory is allocated as needed, avoiding wastage.
- Efficient Insertions and Deletions
- Inserting or deleting elements in a linked list is efficient (O(1) for beginning and O(n) for middle or end).
- No need for shifting elements as in arrays.
- No Wastage of Memory Due to Fixed Size
- Since linked lists grow dynamically, there is no need to reserve extra memory in advance, reducing wastage.
- Implementation of Advanced Data Structures
- Linked lists serve as the foundation for stacks, queues, graphs, and hash tables.
- They are essential for creating circular and doubly linked lists.
- Can Handle Different Data Sizes
- Unlike arrays, where elements must be of the same size, linked lists can have elements of varying sizes.
Disadvantages Of Linked Lists
- Extra Memory Overhead
- Each node requires additional memory for storing a pointer to the next node.
- This increases the overall memory consumption compared to arrays.
- Slower Access Time
- Unlike arrays, where elements can be accessed using an index, linked lists require sequential traversal (O(n)) to access a node.
- Random access is not possible.
- Increased Complexity in Implementation
- Operations like searching and updating take longer compared to arrays.
- Requires careful pointer management to avoid issues like memory leaks and dangling pointers.
- Cache Unfriendliness
- Unlike arrays, which store elements in contiguous memory locations, linked lists use scattered memory.
- This results in poor cache locality and slower performance due to frequent memory access.
Comparison Of Linked Lists And Arrays
In data structures, both linked lists and arrays are used to store collections of data, but they differ in memory management, access speed, and efficiency. Arrays offer fast random access due to their contiguous memory allocation, whereas linked lists allow efficient insertions and deletions because they use dynamic memory allocation.
The table below provides a detailed comparison between Linked Lists and Arrays, highlighting their key differences in terms of memory, performance, and operations.
Feature |
Linked List |
Array |
Memory Allocation |
Dynamically allocated (grows and shrinks as needed). |
Statically or dynamically allocated (fixed size in static arrays). |
Memory Usage |
Extra memory required for pointers (next and previous in doubly linked lists). |
No extra memory required for pointers; only data is stored. |
Data Storage |
Stored in non-contiguous memory locations. |
Stored in contiguous memory locations. |
Element Access |
Sequential access (O(n)), must traverse from the head. |
Direct access (O(1)), using an index. |
Insertion at Beginning |
O(1) (constant time, just update the head pointer). |
O(n) (requires shifting all elements). |
Insertion at End |
O(n) for singly linked lists (traverse to the last node), O(1) if tail pointer is maintained. |
O(1) if space is available, O(n) if resizing is needed. |
Insertion at Middle |
O(n) (requires traversal to find the correct position). |
O(n) (requires shifting elements to create space). |
Deletion at Beginning |
O(1) (just update the head pointer). |
O(n) (requires shifting elements after removal). |
Deletion at End |
O(n) for singly linked lists (traverse to the last node), O(1) if tail pointer is maintained. |
O(1) if no resizing is needed, O(n) if resizing occurs. |
Deletion at Middle |
O(n) (requires traversal to find the correct position). |
O(n) (requires shifting elements to fill the gap). |
Searching |
O(n) (traverse the list from head to find an element). |
O(1) (direct indexing for sorted arrays), O(n) (linear search for unsorted arrays). |
Resizing |
Dynamic resizing, no need to allocate extra space. |
Requires resizing, which involves copying elements to a new memory location. |
Cache Friendliness |
Low (nodes are scattered in memory, affecting CPU cache performance). |
High (stored in contiguous memory, improves CPU cache performance). |
Implementation Complexity |
Complex (requires pointer management). |
Simple (index-based access). |
Data Structure Type |
Linear, but can be modified into non-linear structures (e.g., circular linked lists). |
Strictly linear in nature. |
Best Use Cases |
When frequent insertions/deletions are needed (e.g., dynamic memory allocation, linked stacks, and queues). |
When fast access is required (e.g., look-up tables, matrices, and static lists). |
When To Use A Linked List?
- When insertion and deletion operations are more frequent than access operations.
- When dynamic memory allocation is required without wasting space.
- When implementing stack, queue, graph structures, and hash tables.
When To Use An Array?
- When fast and direct access is needed using indexing.
- When memory efficiency is important (no extra storage required for pointers).
- When the size of the dataset is known and does not change frequently.
Applications Of Linked Lists
Linked lists are widely used in computer science and software development due to their dynamic nature and efficient insertions and deletions. Below are some key applications of linked lists:
1. Dynamic Memory Allocation
- Used in operating systems to manage memory allocation (e.g., heap memory allocation).
- Enables programs to use memory efficiently without knowing the exact size in advance.
2. Implementation of Stacks and Queues
- Stacks (Last In, First Out – LIFO) can be implemented using a singly linked list.
- Queues (First In, First Out – FIFO) can be implemented using a singly or doubly linked list.
3. Managing Graph Data Structures
- Adjacency lists in graphs use linked lists to store neighbors of a node efficiently.
- Used in social networks, navigation systems, and recommendation algorithms.
4. Undo/Redo Functionality in Software
- Applications like text editors, graphic software, and web browsers use doubly linked lists to track changes.
- Users can navigate back and forth using the previous and next pointers.
5. Hash Tables (Collision Handling with Chaining)
- In hash tables, linked lists handle collisions using separate chaining.
- If multiple elements map to the same hash index, a linked list is used to store them.
6. Polynomial Arithmetic and Large Number Calculations
- Polynomial operations (addition, multiplication) use linked lists to store terms dynamically.
- Large numbers (beyond built-in data types) are represented as linked lists to handle operations efficiently.
7. Browser Navigation (Forward and Backward)
- Doubly linked lists are used to store browsing history in web browsers.
- Users can navigate forward and backward easily.
8. Music and Video Playlists
- Circular linked lists are used in media players where the last song connects back to the first song.
- Enables seamless looping of playlists.
9. OS Process Management (Job Scheduling)
- Operating systems use linked lists to manage processes and tasks in schedulers.
- Circular linked lists help in round-robin scheduling.
10. Implementation of LRU (Least Recently Used) Cache
- Used in memory management and database caching.
- Doubly linked lists with hash maps store recently used data for faster access.
Conclusion
Linked lists are a fundamental data structure that offers dynamic memory allocation, efficient insertions, and deletions, making them an essential choice in many real-world applications. Unlike arrays, they do not require predefined memory allocation, allowing flexibility in handling growing and shrinking data structures. However, they come with trade-offs, such as sequential access and extra memory for pointers.
From implementing stacks and queues to managing browser history, process scheduling, and graph representations, linked lists play a crucial role in computer science. Understanding their types, operations, advantages, and applications helps in choosing the right data structure for different scenarios.
While linked lists shine in situations requiring frequent modifications, arrays remain the preferred choice when fast random access and cache efficiency are critical. Thus, the selection between linked lists and arrays depends on the specific problem and performance requirements.
Frequently Asked Questions
Q. When should I use a linked list instead of an array?
Use a linked list when you need frequent insertions and deletions, as it avoids the costly shifting operations required in arrays. If you require fast random access, an array is a better choice.
Q. What is the difference between singly and doubly linked lists?
A singly linked list has nodes with a single pointer pointing to the next node, while a doubly linked list has two pointers (one pointing to the next node and another pointing to the previous node), allowing traversal in both directions.
Q. Why is searching slower in a linked list compared to an array?
In a linked list, elements are stored in non-contiguous memory locations, requiring sequential traversal (O(n)) to find an element. In contrast, arrays allow direct indexing (O(1)) for fast access.
Q. What are the disadvantages of linked lists?
Some disadvantages of linked lists include higher memory usage due to pointers, sequential access (no direct indexing), and increased complexity in implementation compared to arrays.
Q. How does a circular linked list differ from a normal linked list?
In a circular linked list, the last node’s pointer connects back to the first node, forming a loop. This enables continuous traversal and is often used in applications like job scheduling and playlist looping.
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I’m a Computer Science graduate with a knack for creative ventures. Through content at Unstop, I am trying to simplify complex tech concepts and make them fun. When I’m not decoding tech jargon, you’ll find me indulging in great food and then burning it out at the gym.
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