What is the Difference Between Array and Linked List?

Arrays and linked lists are the backbone of countless algorithms and applications in computer science. Yet, many beginners and even experienced programmers find it challenging to differentiate between the two. Is one better than the other? Or are they simply tools for different jobs? In this blog, we’ll break down their fundamental differences,understanding how each works and when to choose one over the other for optimal performance.

What is an Array?

An array is a data structure that allows you to store a collection of elements, all of which must be of the same data type. It is one of the most fundamental and widely used data structures in programming. Arrays enable efficient storage and retrieval of data by organizing elements in a way that allows quick access using an index.

Characteristics of Arrays

1. Contiguous Memory Allocation:
   One of the defining characteristics of an array is that its elements are stored in contiguous memory locations. This means that when an array is created, the computer allocates a continuous block of memory to store all of its elements. This contiguous arrangement allows the program to calculate the memory address of any element quickly using its index, making array operations fast and efficient.

2. Fixed Size:
   The size of an array is fixed once it is created, meaning the number of elements the array can hold is set at the time of initialization.

3. Indexed Access:
   Arrays use zero-based indexing, which means the first element in the array is accessed using index 0, the second element with index 1, and so on. This indexing system allows for constant-time access (O(1)) to any element, making arrays highly efficient for accessing specific data points directly without needing to traverse the entire structure.

4. Homogeneous Data:
   Arrays can only store elements of the same data type. This uniformity makes arrays simple and efficient in terms of memory usage and performance. For instance, an integer array can only hold integers, and a string array can only hold strings. This uniformity helps to avoid complications when performing operations on the data.

If you’re exploring C programming, our blog, Types of Array in C, is your guide to learning about one-dimensional, two-dimensional, and multi-dimensional arrays.

Example

Let’s consider an example where we create an array of integers representing the ages of five people:

ages = [21, 25, 30, 35, 40]

In memory, this array is stored as follows (assuming each integer occupies 4 bytes of memory):

[21] [25] [30] [35] [40]

Here, the array ages has five elements, and each element is stored in a contiguous block of memory. The indices of the array are 0, 1, 2, 3, and 4, corresponding to the values 21, 25, 30, 35, and 40, respectively.

• Accessing the first element (ages[0]) will return 21.
• Accessing the second element (ages[1]) will return 25.
• Accessing the last element (ages[4]) will return 40.

This indexing allows for fast access, as the memory address of any element can be directly calculated based on the index.

Curious about arrays in Python?
Read our blog, Python Array & How To Use Them, for a step-by-step guide with examples.

Use Cases of Arrays

1. Access by Index:
   Arrays excel in situations where you need to frequently access elements by their index. The ability to retrieve any element in constant time (O(1)) makes arrays ideal for performance-critical applications, such as:
   • Storing large datasets that require quick lookups.
   • Implementing caching mechanisms.
   • Managing fixed collections of data (e.g., list of student IDs, product prices).

2. Memory Efficiency for Fixed Data:
   If you know the number of elements you need to store ahead of time and the data size is relatively constant, arrays are an excellent choice. For example, in applications where the data set does not change or grows slowly, such as:
   • Storing scores in a game.
   • Storing fixed lists of constants or settings.

3. Simple Iteration:
   Arrays are particularly efficient when you need to iterate over a set of data. Since the elements are contiguous in memory, iterating over them is fast. Common use cases include:
   • Processing a list of items.
   • Performing mathematical computations on a fixed set of values.
   • Storing and processing data from sensors or logs.

4. Multidimensional Arrays:
   Arrays can also be used to represent multidimensional data structures, such as matrices in mathematical operations or grids in game development. For example, a 2D array can represent a chessboard, where each element in the array corresponds to a square on the board. This capability makes arrays versatile in a variety of applications.

Want to strengthen your understanding of arrays?
Check out our blog, What is an Array?, and master this fundamental data structure.

What is a Linked List?

A linked list is a linear data structure used to store a collection of elements, known as nodes, where each node contains two components: the data and a reference (or pointer) to the next node in the sequence. Each node is dynamically allocated in memory, and the nodes are connected via pointers. This structure makes linked lists highly flexible and efficient in specific scenarios.

Characteristics of Linked Lists

1. Dynamic Memory Allocation:
   Linked lists use dynamic memory allocation, meaning they can grow or shrink during runtime as elements are added or removed. This dynamic nature gives linked lists a significant advantage over arrays, which have a fixed size.

2. Nodes and Pointers:
   A linked list is made up of nodes, where each node contains two parts:

• Data: This is the actual information stored in the node (e.g., an integer, string, or object).
• Pointer: This is a reference to the next node in the list. The pointer links one node to the next, forming a chain of nodes.

The first node is called the head, and the last node’s pointer is typically set to null (or None in Python) to signify the end of the list.

Types of Linked Lists

There are three main types of linked lists, each with different properties and use cases:

1. Singly Linked List:
A singly linked list is the simplest type of linked list, where each node contains a pointer to the next node in the list. The last node’s pointer is null, indicating the end of the list. This structure allows for easy traversal from the head to the last node but doesn’t allow for backward traversal.

Example:

Head -> [Data | Next] -> [Data | Next] -> [Data | Null]

2. Doubly Linked List:
A doubly linked list is a more advanced type where each node contains two pointers: one pointing to the next node and another pointing to the previous node. This bidirectional linking allows for easier traversal in both directions (forward and backward), making certain operations more efficient.

Example:

Head <-> [Prev | Data | Next] <-> [Prev | Data | Next] <-> [Prev | Data | Null]

3. Circular Linked List:
A circular linked list is similar to a singly or doubly linked list, but with a key difference: in a singly circular linked list, the last node’s pointer points back to the head node, creating a circular structure. In a doubly circular linked list, the last node points to the head, and the head’s previous pointer points to the last node, allowing traversal in both directions while keeping the circular connection intact.

Example of Singly Circular Linked List:

Head -> [Data | Next] -> [Data | Next] -> [Data | Head]

Example

Let’s consider an example of a singly linked list containing the values 10, 20, and 30.

The linked list structure looks like this:

Head -> [10 | Next] -> [20 | Next] -> [30 | Null]

In this case:

• The first node stores the value 10 and points to the next node, which stores 20.
• The second node stores 20 and points to the next node, which stores 30.
• The last node stores 30 and its pointer is null, indicating the end of the list.

This dynamic structure allows for efficient insertions and deletions, as you can simply adjust the pointers rather than shifting elements in memory (as you would with arrays).

Use Cases of Linked Lists

1. Dynamic Data Structures:
   Linked lists are ideal for applications where the number of elements is not known beforehand or changes frequently. This makes linked lists suitable for:
   • Implementing queues, stacks, and other abstract data types.
   • Representing flexible data structures like graphs or trees.

2. Efficient Insertions and Deletions:
   Linked lists allow for efficient insertions and deletions, especially when adding or removing elements at the beginning or middle of the list. 

3. Memory Efficiency:
   Linked lists are more memory-efficient than arrays because they do not allocate extra memory upfront. Each node is allocated only when needed, making them ideal for situations where memory usage needs to be optimized. For example:
   • Implementing dynamic data structures like hash tables, where each bucket may have a varying number of elements.
   • Storing elements in systems with highly dynamic datasets, such as real-time systems or databases.

4. Circular Structures:
   Circular linked lists are used when you need to loop through a list repeatedly without explicitly checking for the end. Common applications include:
   • Implementing round-robin scheduling in operating systems.
   • Circular queues, where elements are processed in a continuous loop.

Learn how to reverse a linked list in Java step by step! If you want to know more, read our blog: How to Reverse Linked List in Java?

Key Differences Between Arrays and Linked Lists

When to Use Arrays vs. Linked Lists?

Both arrays and linked lists are fundamental data structures, but their strengths and weaknesses make them suitable for different use cases. 

Here’s a breakdown of scenarios where one might be more advantageous over the other:

Scenarios Where Arrays Are a Better Choice

1. When You Need Fast Access to Elements (Direct Indexing):
   • Use Case: If you need to frequently access elements by their index, such as in situations where data is being read in bulk or when you know the index of the element you need to access.
   • Example: Accessing a specific element from a list of student grades, where you need to access a student’s grade using their ID (index).

2. When You Have a Fixed Size Dataset:
   • Use Case: When the size of your data is known beforehand and won’t change over time, such as in systems where data is pre-loaded or has a static structure.
   • Example: A list of items in an inventory system where the maximum number of items is fixed.

3. When You Need Efficient Memory Utilization for Fixed-Size Data:
   • Use Case: If the number of elements in your dataset is predetermined and constant, arrays can be a memory-efficient choice, without the need for additional pointers or overhead.
   • Example: Storing a fixed number of sensor readings for a daily temperature log, where the size of the array is known in advance.

Scenarios Where Linked Lists Are a Better Choice

1. When You Need Dynamic Size:
   • Use Case: If your dataset size will change frequently and you do not know the number of elements in advance, linked lists can dynamically allocate memory as needed without any resizing issues.
   • Example: A social media platform where users are adding and removing friends regularly, and the number of friends varies greatly from user to user.

2. When You Need Efficient Insertions and Deletions:
   • Use Case: Linked lists are ideal when you need to frequently insert or delete elements, particularly at the beginning or middle of the list, without shifting the rest of the data.
   • Example: Implementing a queue or stack data structure, where you frequently add or remove items at the front or rear (e.g., browser history management).

3. When You Need to Minimize Memory Wastage:
   • Use Case: If memory usage needs to be flexible and not pre-allocated, linked lists allow you to allocate memory as needed, avoiding the wastage seen in arrays with fixed sizes.
   • Example: A program where the number of users in a chat application is unpredictable, and memory needs to grow or shrink dynamically based on the number of users.

Practical Examples of Arrays and Linked Lists

Example Use Cases in Real-World Applications

Arrays:

1. E-commerce Website Product Listings:
   • Scenario: An e-commerce website displays a fixed set of products for a category.
   • Use Case: Arrays can be used to store product details like name, price, and description. Since the number of products in a category doesn’t change frequently, arrays provide fast access and efficient memory usage.

2. Image Processing:
   • Scenario: An image is represented as a 2D matrix where each element represents a pixel’s color.
   • Use Case: Arrays are perfect for storing pixel values, where access to any pixel by its index (row and column) is needed. Image manipulation (e.g., applying filters) often relies on arrays for fast, direct access to pixel data.

3. Static Data Structures in Banking:
   • Scenario: An array is used to represent an account balance history for a fixed period (e.g., 12 months).
   • Use Case: The array stores the monthly balances, and the number of months is fixed. The array allows for quick access to specific months.

Linked Lists:

1. Task Scheduling in Operating Systems:
   • Scenario: A task scheduler maintains a list of processes to be executed.
   • Use Case: A linked list can be used to dynamically add or remove tasks as they arrive or are completed, and tasks can be executed in a specific order.

2. Navigation History in Web Browsers:
   • Scenario: Web browsers maintain a history of visited pages.
   • Use Case: A doubly linked list can store visited web pages where each node points to the next and previous pages, allowing easy back-and-forth navigation.

3. Real-Time Stock Price Updates:
   • Scenario: A stock trading application that maintains real-time stock price data.
   • Use Case: A linked list can be used to dynamically add new stock prices as they are received, allowing the list to grow or shrink based on the volume of trades.

Want to master linked lists in C++?
Read our blog, LINKED LIST IN C++, and get a complete guide to understanding and using linked lists in C++.

Code Snippets Demonstrating Array and Linked List Usage

Array Example:

Let’s use an array to store and retrieve a list of product names in an e-commerce application.

# Array to store product names

products = ["Laptop", "Smartphone", "Tablet", "Smartwatch", "Headphones"]

# Accessing elements by index

print("First product:", products[0])  # Laptop

print("Second product:", products[1])  # Smartphone

# Updating a product

products[2] = "Wireless Charger"

print("Updated products list:", products)

# Iterating over array elements

print("Product list:")

for product in products:

    print(product)

Explanation:

• The array products holds a fixed set of product names.
• We access the elements directly using the index (O(1) access).
• The list is updated by modifying an element at a specific index.

Linked List Example:

Here’s how a simple singly linked list can be implemented to store tasks in a task scheduler.

# Node class representing a task in a linked list

class Node:

    def __init__(self, task):

        self.task = task  # task data

        self.next = None  # pointer to the next task

# Linked list class

class LinkedList:

    def __init__(self):

        self.head = None

    # Add task to the end of the list

    def append(self, task):

        new_node = Node(task)

        if not self.head:

            self.head = new_node

            return

        last_node = self.head

        while last_node.next:

            last_node = last_node.next

        last_node.next = new_node

    # Print the tasks in the list

    def print_tasks(self):

        current = self.head

        while current:

            print(current.task)

            current = current.next

# Creating and using the linked list

task_list = LinkedList()

task_list.append("Task 1: Complete report")

task_list.append("Task 2: Review code")

task_list.append("Task 3: Attend meeting")

# Print all tasks

print("Task List:")

task_list.print_tasks()

Explanation:

• The Node class holds the task name and a pointer to the next task (next).
• The LinkedList class has methods to append tasks and print them.
• Tasks are added dynamically to the linked list, and the list is printed by traversing through each node.

How To Get Started With Learning Array and Linked List?

The Full Stack Software Development: Building Scalable Cloud Applications course offered by Great Learning in collaboration with the McCombs School of Business, University of Texas at Austin, is designed to equip learners with industry-relevant skills for creating robust, scalable, and efficient cloud-based applications. This course integrates modern software development practices with a focus on both front-end and back-end development, ensuring participants gain holistic expertise in building end-to-end applications.

Course Highlights

• Comprehensive Curriculum:
  • Learn essential programming languages such as JavaScript and Python.
  • Master front-end frameworks like React and back-end technologies such as Node.js.
  • Dive into cloud technologies like AWS and Google Cloud for deploying scalable applications.

• Hands-On Projects:
  • Build real-world applications to apply theoretical knowledge.
  • Work on full-stack development projects, focusing on scalable architectures.

• Guidance from Experts:
  • Benefit from mentorship by faculty from the McCombs School of Business and industry professionals.
  • Get personalized feedback on projects and assignments.

How It Relates to Arrays and Linked Lists?

In the context of building scalable cloud applications, understanding data structures like arrays and linked lists is foundational. These structures are essential in developing efficient algorithms and ensuring application performance, particularly in the following scenarios:

Applications of Arrays

• Data Retrieval: Arrays are frequently used in scenarios requiring fast, indexed access to data, such as loading user dashboards or managing tabular data.

• Static Data Storage: Arrays are ideal for storing fixed-size configurations like application themes or predefined roles (e.g., “admin,” “user”).

Applications of Linked Lists

• Dynamic User Management: In scenarios where user roles or tasks change dynamically (e.g., in collaborative tools), linked lists provide flexibility in adding or removing elements without significant overhead.

• Task Queues in Microservices: Linked lists can manage queues for tasks in cloud-based architectures, where dynamic memory allocation is crucial.

How the Course Prepares You for These Applications?

1. Understanding Data Structures:
   • Gain a deep understanding of arrays, linked lists, and other data structures critical for solving real-world problems.

2. Cloud-Based Development:
   • Learn how to leverage cloud platforms to manage large-scale applications where efficient memory utilization and scalability are key.

3. Building Scalable Architectures:
   • Learn techniques for structuring your application to optimize performance, leveraging the right data structures and algorithms.

4. Practical Applications:
   • Through hands-on projects, you’ll implement data structures in the context of cloud applications, enhancing your ability to choose the right tools for specific scenarios.

Conclusion

Understanding the differences between arrays and linked lists is fundamental for designing efficient software solutions. While arrays excel in scenarios requiring fast, indexed access and fixed-size storage, linked lists shine in dynamic environments where flexibility in memory allocation is crucial. The choice between these data structures depends on the specific needs of your application, such as performance, scalability, and memory utilization.

For aspiring developers, mastering these concepts is essential, and courses like Full Stack Software Development: Building Scalable Cloud Applications by Great Learning offer the perfect platform to learn, implement, and apply these principles in real-world cloud-based scenarios. Equip yourself with the skills to build scalable and efficient applications and stay ahead in the fast-paced tech industry.