Linked Lists are linear data structures where elements are stored in nodes, and each node points to the next node in the sequence. Unlike arrays, they allow for efficient insertions and deletions in the middle of the list. In coding interviews, questions about linked lists test a candidate's grasp on pointer manipulation and understanding of sequential data storage.

Check out our carefully selected list of basic and advanced Linked Lists questions and answers to be well-prepared for your tech interviews in 2024.

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A Linked List is a dynamic data structure ideal for fast insertions and deletions. Unlike arrays, its elements aren't stored contiguously but are linked via pointers.

A Linked List is a collection of nodes, each having:

• Data: The stored value.
• Next Pointer: A reference to the next node.

The list starts with a Head node and ends with a node having a null Next pointer.

• Dynamic Size: Adapts to data volume.
• Non-Contiguous Memory: Flexibility in storage.
• Fast Insertions/Deletions: Limited pointer adjustments needed.

1. Singly Linked List: Each node has a single pointer to the next node. Traversal is unidirectional: from head to tail.
2. Doubly Linked List: Each node have two pointers: one pointing to the next node and another to the previous node. This allows for bidirectional traversal.
3. Circular Linked List: Like a singly linked list, but the tail node points back to the head, forming a loop.
4. Multi-level Linked List: This specialized type has nodes with multiple pointers, each pointing to different nodes. It's often used in advanced data structures like multi-level caches.

• Traversal: Scan through nodes — $O(n)$.
• Insertion at the Beginning: Add a node at the start — $O(1)$.
• Insertion (other cases)/Deletion: Add or remove nodes elsewhere in the list — $O(n)$.
• Search: Locate specific nodes — $O(n)$.
• Sorting: Order or organize nodes in the list. Commonly-used algorithms for linked lists like merge sort have a time complexity of $O(n \log n)$.
• Merging: Combine two lists — $O(n)$ where $n$ is the total number of nodes in both lists.
• Reversal: Flip node order — $O(n)$.

Here is the Python code:

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class LinkedList:
    def __init__(self):
        self.head = None

    def insert(self, data):
        new_node = Node(data)
        if not self.head:
            self.head = new_node
        else:
            last_node = self.head
            while last_node.next:
                last_node = last_node.next
            last_node.next = new_node

    def display(self):
        current = self.head
        while current:
            print(current.data)
            current = current.next

# Usage
my_list = LinkedList()
my_list.insert(1)
my_list.insert(2)
my_list.insert(3)
my_list.display()

# Output:
# 1
# 2
# 3

Linked lists are widely used in real-world applications for their advantages in dynamic memory management and data manipulation.

• Task Scheduling: Linked lists efficiently manage queues of tasks awaiting execution.
• Memory Management: They facilitate dynamic memory allocation, especially useful in applications like memory pool management.

• Undo/Redo Functionality: Editors maintain a stack of changes using linked lists, enabling the undo and redo functionalities.

• Playlists: Linked lists offer flexibility in managing playlists, allowing for the easy addition, deletion, and navigation of tracks.

• Browser History: Linked lists, especially doubly linked ones, are instrumental in navigating web page histories, permitting both forward and backward traversal.

• Symbol Tables: Compilers employ linked lists to manage tables containing variable and function identifiers. This provides scoped access to these identifiers during different stages of compilation.

• Transient Storage Structures: While core storage might use trees or hash indexes, linked lists can serve auxiliary roles, especially in in-memory databases.

• Graph Representation: Algorithms requiring graph representations often utilize adjacency lists, essentially arrays of linked lists, to depict vertices and edges.

• LRU Cache: Linked lists, particularly doubly linked ones, play a pivotal role in the Least Recently Used (LRU) caching algorithms to determine which items to replace.

• Packet Management: In networking scenarios, linked lists help manage queues of data packets awaiting transmission.

• Character Inventory: In role-playing games, a character's inventory, where items are added and removed frequently, can be managed using linked lists.

Let's look at the pros and cons of using linked lists compared to arrays.

• Dynamic Size: Linked lists naturally adjust to changing sizes, while arrays are fixed-sized. Dynamic arrays auto-resize but can lag in efficiency during frequent mid-list insertions or deletions.

• Efficient Insertions/Deletions: Insertions and deletions in linked lists only require a few pointer adjustments, whereas arrays may need shifting of elements.

• Flexibility in Size: Memory for nodes in linked lists is allocated or released as needed, potentially reducing memory wastage.

• Merging and Splitting: It's simpler to merge or split linked lists.

• Memory Overhead: Each node has overhead due to data and a pointer, using more memory than arrays for the same number of elements.

• Sequential Access: Linked lists only allow sequential access, unlike arrays that support direct indexing.

• Cache Inefficiency: Nodes might be scattered in memory, leading to cache misses.

• No Random Access: Element retrieval might require full list traversal, whereas arrays offer constant-time access.

• Data Integrity: If a node's link breaks, subsequent nodes are lost.

• Search Efficiency: Requires linear scans, which can be slower than searches in sorted arrays or trees.

• Sorting: Certain sorting algorithms, like QuickSort, are less efficient with linked lists than with arrays.

Doubly linked lists offer advantages in specific use-cases but use more memory and may require more complex thread-safety

• Deletion: If only the node to be deleted is known, doubly linked lists can delete it in $O(1)$ time, whereas singly linked lists may take up to $O(n)$ to find the prior node.

• Tail Operations: In doubly linked lists, tail-related tasks are $O(1)$. For singly linked lists without a tail pointer, these are $O(n)$.

• Cache Implementations: Doubly linked lists are ideal due to quick bidirectional insertion and deletion.

• Text Editors and Undo/Redo: The bidirectional capabilities make doubly linked lists more efficient for these functions.

Array-based stacks excel in time efficiency and direct element access. In contrast, linked list stacks are preferable for dynamic sizing and easy insertions or deletions.

• Speed of Operations: Both pop and push are $O(1)$ operations.
• Memory Use: Both have $O(n)$ space complexity.
• Flexibility: Both can adapt their sizes, but their resizing strategies differ.

• Locality: Consecutive memory locations benefit CPU caching.
• Random Access: Provides direct element access.
• Iterator Needs: Preferable if indexing or iterators are required.
• Performance: Slightly faster for top-element operations and potentially better for time-sensitive tasks due to caching.
• Push: $O(1)$ on average; resizing might cause occasional $O(n)$.

• Memory Efficiency: Better suited for fluctuating sizes and limited memory scenarios.
• Resizing Overhead: No resizing overheads.
• Pointer Overhead: Requires extra memory for storing pointers.

Here is the Python code:

class ArrayBasedStack:
    def __init__(self):
        self.stack = []
    def push(self, item):
        self.stack.append(item)
    def pop(self):
        return self.stack.pop() if self.stack else None

Here is the Python code:

class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None
class LinkedListStack:
    def __init__(self):
        self.head = None
    def push(self, item):
        new_node = Node(item)
        new_node.next = self.head
        self.head = new_node
    def pop(self):
        if self.head:
            temp = self.head
            self.head = self.head.next
            return temp.data
        return None

A circular Linked List is a specific type of linked list where the tail node is intentionally connected back to the head node to form a closed loop.

• Emulating Circular Structures: Useful for representing naturally circular data like polygon vertices, buffer pools, or round-robin scheduling in operating systems.

• Queue Efficiency: Accessing the front and rear elements in constant time, improving queue implementations.

• Algorithmic Simplifications: Enables easier data manipulations like list splitting and concatenation in constant time.

Here is the Python code:

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class CircularQueue:
    def __init__(self):
        self.front = self.rear = None

    def enqueue(self, data):
        new_node = Node(data)
        if self.rear:
            self.rear.next, self.rear = new_node, new_node
        else:
            self.front = self.rear = new_node
        self.rear.next = self.front

    def dequeue(self):
        if not self.front: return None
        if self.front == self.rear: self.front = self.rear = None
        else: self.front = self.front.next; self.rear.next = self.front
        return self.front.data if self.front else None

# Example usage:
cq = CircularQueue()
cq.enqueue(1); cq.enqueue(2); cq.enqueue(3)
print(cq.dequeue(), cq.dequeue(), cq.dequeue(), cq.dequeue())

While both QuickSort and MergeSort are powerful sorting algorithms, MergeSort is often favored for linked lists for a variety of reasons, including its stability and optimized disk I/O operations.

• No Need for Random Access: Unlike QuickSort, which benefits from direct access to elements for efficient partitioning, MergeSort doesn't require random access, making it ideal for linked lists.

• Cache Efficiency: MergeSort sequentially accesses elements, optimizing CPU cache usage, especially with large data sets.

• Optimized Disk Operations: MergeSort performs fewer disk I/O operations when sorting data that doesn't fit in memory, outperforming QuickSort in such scenarios.

Here is the Python code:

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

def merge_sort(head):
    if not head or not head.next:
        return head
    
    mid = get_middle(head)
    next_to_mid = mid.next
    mid.next = None
    
    left = merge_sort(head)
    right = merge_sort(next_to_mid)
    
    return merge(left, right)

def get_middle(head):
    slow, fast = head, head
    while fast.next and fast.next.next:
        slow, fast = slow.next, fast.next.next
    return slow

def merge(left, right):
    dummy = Node(0)
    curr = dummy
    
    while left and right:
        if left.data < right.data:
            curr.next, left = left, left.next
        else:
            curr.next, right = right, right.next
        curr = curr.next
    
    curr.next = left or right
    return dummy.next

While it may not be possible to traverse a linked list in better than $O(n)$ time complexity in the strictest sense, there are techniques that can make the traversal process more efficient in certain contexts.

In particular, let's explore the idea of "Jump Pointers" or "Square Root Jumps" which allows you to traverse a linked list in $O(\sqrt{n})$ time complexity.

Jump Pointers allow for quicker traversal by "jumping" over a fixed number of nodes $k$ during each step. This reduces the total number of nodes visited, thereby improving the time complexity.

For instance, when $k = \sqrt{n}$, the traversal time complexity drops to $O(\sqrt{n})$.

Here is the Python code:

# Node definition
class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None

# LinkedList definition
class LinkedList:
    def __init__(self):
        self.head = None

    # Add node to end
    def append(self, data):
        new_node = Node(data)
        if not self.head:
            self.head = new_node
            return
        last = self.head
        while last.next:
            last = last.next
        last.next = new_node

    # Traverse using Jump Pointers
    def jump_traverse(self, jump_size):
        current = self.head
        while current:
            print(current.data)
            for _ in range(jump_size):
                if not current:
                    return
                current = current.next

# Create linked list and populate it
llist = LinkedList()
for i in range(1, 11):
    llist.append(i)

# Traverse using Jump Pointers
print("Jump Pointer Traversal:")
llist.jump_traverse(int(10**0.5))

While Binary Search boasts a time complexity of $O(\log n)$, applying it to a singly linked list is less straightforward due to the list's linear nature and $O(n)$ access time. However, Skip Lists offer a clever workaround to achieve sub-linear search times in linked lists.

In a singly linked list, random access to elements is not possible. To reach the $k$-th element, you have to traverse $k-1$ preceding nodes. Therefore, the act of accessing a middle element during binary search incurs a time complexity of $O(n)$.

Skip Lists augment sorted linked lists with multiple layers of 'express lanes', allowing you to leapfrog over sections of the list. Each layer contains a subset of the elements from the layer below it, enabling faster search.

By starting at the topmost layer and working downwards, you can reduce the search space at each step. This results in an average time complexity of $O(\log n)$ for search operations.

Here is the Python code:

# Define a node for the Skip List
class SkipNode:
    def __init__(self, value):
        self.value = value
        self.next = []

# Initialize a Skip List
class SkipList:
    def __init__(self):
        self.head = SkipNode(float('-inf'))  # Initialize with the smallest possible value
        self.levels = 1  # Start with a single level

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists

👉🏼 Check out all 31 answers here: Devinterview.io - Linked Lists